33DETAILED ACTION
Claim Objections
The amendments filed 5/11/2026 have been reviewed, Claim 30 is objected to because of the following informalities: claim 30 has been canceled, so that claim 30 in 5/11/2026 should be deleted.
. Appropriate correction is required.
Response to Arguments
. The amendments filed 5/11/2026, including claims 1-8, 15, 17-23, and 31-33, and newly added claim 91, excluding above objected claim 30, have be entered and recorded.
Applicant's amendments and corresponding arguments filed 5/11/2026 have been fully considered, but are moot in view of the new ground(s) of rejection because the Applicant has substantially amended at least independent claims 1, 15 and 31 and Applicant's arguments in view of the amendments been fully considered, but are not persuasive:
Re Claim 1, first, regarding to the amended limitations “training a second machine learning model to identify an expected distribution of mapping categories for a geographic area at a given time”, and “each overhead image of the respective pluralities of overhead images comprises a respective plurality of pixels, and a first machine learning model outputs a designation and likelihood of each pixel as being of a particular mapping category of a plurality of mapping categories”, and “training, using the processing circuitry and based on the pixel designations and likelihoods output by the first machine learning in association with the plurality of maps, the second machine learning model to identify the expected distribution and temporal patterns for the mapping categories of the geographic area at the given time”;
and above amendments require “training a second machine learning model, based on 1) on the pixel designations and likelihoods output by the first machine learning in association with 2) the plurality of maps;
and, Applicant states (in pages 9-10 of the Arguments and Remarks of 5/11/26) that cited references, Gibson as modified by Veronesi and Jha do not disclose, a first machine learning model, and a second learning model as required in the amended claims, particularly training the second learning model, based on the pixel designations and likelihoods output by the first machine learning in association with the plurality of maps, to identify the expected distribution and temporal patterns for the mapping categories of the geographic area at the given time;
however, the Examiner disagrees, because:
Gibson discloses “a first machine learning model”, in claimed limitation “each overhead image of the respective pluralities of overhead images comprises a respective plurality of pixels, and a first machine learning model outputs a designation and likelihood of each pixel as being of a particular mapping category of a plurality of mapping categories (see Gibson: e.g., -- computer-implemented methods and systems are provided for pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system. To do this, the amount that each class is contained within each area image relative to the whole dataset is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken-, in abstract, and, --to obtain and update the vector features of the topographic data is to process aerial imagery and extract the vector information therefrom. To do this, machine learning systems can be trained to do this automatically… A deep learning model needs to be “trained” by showing it samples of imagery along with labels stating what class each pixel of the imagery belongs to.--, in [0004]-[0006]; also see: -- computer-implemented methods and systems for generating training data for training a machine learning system to automatically extract topographic features. In particular, aspects described herein relate to a method of pre-processing image data to generate improved training data.--, in [0001], and, -- pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system, with each image containing a part of the geographic region of interest. To do this, the amount that each class is contained within each area image relative to the whole dataset (i.e., the whole geographic region captured) is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken. This ensures that the distribution of patches taken from each area prioritises features that are less common….. a computer-implemented method of processing image data for use in training a machine learning system for classifying image data, the method comprising obtaining image data comprising a plurality of images, each image corresponding to a respective geographic area, processing the image data to identify one or more classes of topographic feature contained within each image, wherein processing the image data comprises determining a quantity of each respective topographic feature class contained within each image, generating a first dataset comprising a set of values for each image, wherein each value is representative of the quantity of one of the plurality of topographic feature classes contained within the respective image, and processing the first dataset to determine a number of samples required from each image for each topographic feature class. Processing the first dataset comprises (i) generating a normalised dataset, wherein the values of the first dataset are normalised across each image and each topographic feature class, and (ii) for each topographic feature class, calculating the number of samples required from each image based on the normalised dataset and a target number of samples for the respective topographic feature class. --, in [0008]-[0010]; -- [0060] The result is a probability distribution which ensures for each class, a certain number of patches will be selected containing each class. That is to say, it ensures the existence of the class is present in X patches.--, in [0060]; and, -- [0069] An example of this approach is illustrated by FIG. 5, which shows three candidate patches X, Y and Z. The distribution processing has determined that two patches of vegetation class 50 and two patches of building class 52 are needed for input to the machine learning system. The patches are selected by the least prevalent class, filling the distribution until full. In this case, the vegetation class 50 makes up 80% and the building class 52 makes up 20%, and so patches Y and Z are selected first in order to provide the two patches of building class 52 required by the distribution. This leaves only patch X for the vegetation class 50, and so patch X will be selected, along with an augmented patch X′, to thereby provide the two patches of vegetation class 50 required by the distribution.--, in [0069];
and, --and a topographic extraction program 738, which implement different aspects described herein when run by the CPU 702. Specifically, the distribution calculation program 734 is configured to receive the classification data 722 and image data 724 to perform the method of optimizing the distribution of sample areas as described with reference to FIG. 3, from which the distribution data 726 is generated and stored…to perform the method of selecting a training dataset from the patches available to meet a target allocation described with reference to FIG. 5, from which allocation data 728 and thus training data 730 is generated and stored. Similarly, the topographic feature extraction program 738 is configured to receive the training data 730, from which it is trained to classify images.--, in [0099]);
So that, Gibson discloses a first learning model to process the received overhead images to extract features, to label pixels to classification data, and generate training data, Gibson’s learning model read on claimed limitation of “a first machine learning model outputs a designation and likelihood of each pixel as being of a particular mapping category of a plurality of mapping categories”;
Veronesi discloses training a second machine learning model, based on 1) the pixel designations and likelihoods output by the first machine learning {as discussed above of Gibson’s disclosures} in association with 2) the plurality of maps, to identify the expected distribution and temporal patterns for the mapping categories of the geographic area at the given time (see Veronesi: e.g., --receiving a point cloud of a digital surface model of the wooded area; concatenating data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in abstract;
--the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]);
{apparently, Veronesi’s machine learning model as the second machine learning model is different from Gibson’s machine learning model as the first learning model, at least Veronesi’s learning model takes input including 1) labeled data, feature vectors, and topographic variables of geographic regions {with time, seasonal changes}, and output as “to generate timber data for each of the plurality of pixels of the image; and generating an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image”, which read on claimed output of the second learning model as “to identify the expected distribution and temporal patterns for the mapping categories of the geographic area at the given time”;
The cited references Gibson as modified by Veronesi and Jha disclose, a first machine learning model, and a second learning model as required in the amended claims.
Therefore, amended claims 1-8, 15, 17-23, and 31-33, and 91 are still not patentably distinguishable over the prior art reference(s). Further discussions are addressed in the prior art rejection section below.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-8, 15, 17-23, and 31-33 are rejected under 35 U.S.C. 103 as being unpatentable over Gibson (US 20240020950 A1), and in view of Veronesi et al. (US 20230102406 A1), and further in view of Jha (US 20210209424 A1).
Re Claim 1: Veronesi discloses a method of training a second machine learning model to identify an expected distribution of mapping categories for a geographic area at a given time (see Veronesi: e.g., --receiving a point cloud of a digital surface model of the wooded area; concatenating data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in abstract;
--the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]); comprising:
receiving, by processing circuitry, a plurality of maps of the geographic area (see Veronesi: e.g., --the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]), wherein:
each map of the plurality of maps is generated based on a respective plurality of overhead images (see Veronesi: e.g., --the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]);
Veronesi however still does not explicitly disclose that a plurality of overhead images captured during a respective portion of a time period,
Jha discloses a plurality of overhead images captured during a respective portion of a time period (see Jha: e.g., -- a computer-implemented method for generating land use land cover (LULC) classification of a geographic area is described. The computer-implemented method comprises receiving a first input defining a geographic area and a first time frame. The computer-implemented method further comprises automatically retrieving a first set of satellite images corresponding to the geographic area and the first time frame. The computer-implemented method further comprises automatically classifying the first set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting the LULC classification of the geographic area.
[0007] According to an example, the plurality of land use land cover (LULC) classes may include at least one of vegetation cover, surface water cover, built-up area, barren/open land, and cropland.
[0008] According to an example, the computer-implemented method may further comprise creating a training set including a plurality of satellite images, and automatically training a deep learning model using the training set and a neural network to develop the trained deep learning model.
[0009] According to an example, creating a training set may further comprise automatically retrieving a plurality of satellite images corresponding to a plurality of geographic areas, automatically fetching a plurality of spectral bands corresponding to the plurality of satellite images, automatically processing the plurality of spectral bands to convert digital number of each pixel of the plurality of spectral bands into reflectance or radiance values, and creating the training set in the form of creating pixel-wise shapefiles corresponding to each of the plurality of LULC classes.--, in [0006]-[0009]; and, -- the computer-implemented method may further comprise receiving a second input defining a second time frame, automatically retrieving a second set of satellite images corresponding to the geographic area and the second time frame, automatically classifying the second set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting a comparison of the land use land cover (LULC) classes of the first and the second set of satellite images, the comparison illustrating a quantitative relative change in the land use land cover (LULC) classes of the geographic area over a time duration from the first time frame to the second time frame.
[0017] According to another exemplary embodiment, a system for generating land use land cover (LULC) classification of a geographic area is described. The system comprises at least one processor and at least one computer readable memory coupled to the at least one processor, and the processor is configured to perform all or some steps of the method described above.
[0018] According to another exemplary embodiment, a non-transitory computer readable medium is described. The non-transitory computer readable medium comprises a computer-readable code comprising instructions, which when executed by a processor, causes the processor to perform all or some steps of the method described above.
[0019] It is an object of the invention to provide a Geo-spatial artificial intelligence (Geo-AI) based fully automated computer-based method and system for predicting land use land cover (LULC) classification of a geographic area using a trained deep learning model--, in [0016]-[0019]);
Veronesi and Jha are combinable as they are in the same field of endeavor: machine learning algorithms used in geographic mapping and images processing. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Veronesi’s method using Jha’s teachings by including a plurality of overhead images captured during a respective portion of a time period to Veronesi’s geographic mapping in order to determine a quantitative relative change in classes of the geographic area over a time duration from the first time frame to the second time frame (see Jha: e.g., in abstract, [00006]-[0009], and [0016]-[0019]);
Veronesi as modified by Jha further disclose each overhead image of the respective pluralities of overhead images comprises a respective plurality of pixels (see Veronesi: e.g., --the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, -- The computer-executable instructions when executed by a processor, cause the processor to perform a process including receiving an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving a set of climate data for a geographic region in which the wooded area is located; receiving a point cloud of a digital surface model of the wooded area; concatenating data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0007]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]);
Veronesi as modified by Jha however still do not explicitly disclose that a first machine learning model outputs a designation and likelihood of each pixel as being of a particular mapping category of a plurality of mapping categories;
Gibson discloses a first machine learning model outputs a designation and likelihood of each pixel as being of a particular mapping category of a plurality of mapping categories (see Gibson: e.g., -- computer-implemented methods and systems are provided for pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system. To do this, the amount that each class is contained within each area image relative to the whole dataset is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken-, in abstract, and, --to obtain and update the vector features of the topographic data is to process aerial imagery and extract the vector information therefrom. To do this, machine learning systems can be trained to do this automatically… A deep learning model needs to be “trained” by showing it samples of imagery along with labels stating what class each pixel of the imagery belongs to.--, in [0004]-[0006]; also see: -- computer-implemented methods and systems for generating training data for training a machine learning system to automatically extract topographic features. In particular, aspects described herein relate to a method of pre-processing image data to generate improved training data.--, in [0001], and, -- pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system, with each image containing a part of the geographic region of interest. To do this, the amount that each class is contained within each area image relative to the whole dataset (i.e., the whole geographic region captured) is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken. This ensures that the distribution of patches taken from each area prioritises features that are less common….. a computer-implemented method of processing image data for use in training a machine learning system for classifying image data, the method comprising obtaining image data comprising a plurality of images, each image corresponding to a respective geographic area, processing the image data to identify one or more classes of topographic feature contained within each image, wherein processing the image data comprises determining a quantity of each respective topographic feature class contained within each image, generating a first dataset comprising a set of values for each image, wherein each value is representative of the quantity of one of the plurality of topographic feature classes contained within the respective image, and processing the first dataset to determine a number of samples required from each image for each topographic feature class. Processing the first dataset comprises (i) generating a normalised dataset, wherein the values of the first dataset are normalised across each image and each topographic feature class, and (ii) for each topographic feature class, calculating the number of samples required from each image based on the normalised dataset and a target number of samples for the respective topographic feature class. --, in [0008]-[0010]; -- [0060] The result is a probability distribution which ensures for each class, a certain number of patches will be selected containing each class. That is to say, it ensures the existence of the class is present in X patches.--, in [0060]; and, -- [0069] An example of this approach is illustrated by FIG. 5, which shows three candidate patches X, Y and Z. The distribution processing has determined that two patches of vegetation class 50 and two patches of building class 52 are needed for input to the machine learning system. The patches are selected by the least prevalent class, filling the distribution until full. In this case, the vegetation class 50 makes up 80% and the building class 52 makes up 20%, and so patches Y and Z are selected first in order to provide the two patches of building class 52 required by the distribution. This leaves only patch X for the vegetation class 50, and so patch X will be selected, along with an augmented patch X′, to thereby provide the two patches of vegetation class 50 required by the distribution.--, in [0069];
and, --and a topographic extraction program 738, which implement different aspects described herein when run by the CPU 702. Specifically, the distribution calculation program 734 is configured to receive the classification data 722 and image data 724 to perform the method of optimizing the distribution of sample areas as described with reference to FIG. 3, from which the distribution data 726 is generated and stored…to perform the method of selecting a training dataset from the patches available to meet a target allocation described with reference to FIG. 5, from which allocation data 728 and thus training data 730 is generated and stored. Similarly, the topographic feature extraction program 738 is configured to receive the training data 730, from which it is trained to classify images.--, in [0099]);
Veronesi (as modified by Jha) and Gibson are combinable as they are in the same field of endeavor: machine learning algorithms used in geographic mapping and images processing. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Gibson’s method using Veronesi’s teachings by including training a second machine learning model to identify an expected distribution of mapping categories for a geographic area at a given time to Gibson’ geographic mapping in order to apply a machine learning model using the feature vector to generate geographic mapping for each of the plurality of pixels of the images for the right time and high accuracy (see Veronesi: e.g., in abstract, [00003]-[0005], [0021], [0025]-[0028], and [0058]);
Veronesi as modified by Jha and Gibson further training, using the processing circuitry and based on the pixel designations and likelihoods output by the first machine learning in association with the plurality of maps, the second machine learning model to identify the expected distribution and temporal patterns for the mapping categories of the geographic area at the given time (see Veronesi: e.g., --receiving a point cloud of a digital surface model of the wooded area; concatenating data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in abstract;
--the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028];
{apparently, Veronesi’s machine learning model as the second machine learning model is different from Gibson’s machine learning model as the first learning model, at least Veronesi’s learning model takes input including 1) labeled data, feature vectors, and topographic variables of geographic regions {with time, seasonal changes}, and output as “to generate timber data for each of the plurality of pixels of the image; and generating an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image”, which read on claimed output of the second learning model as “to identify the expected distribution and temporal patterns for the mapping categories of the geographic area at the given time”}).
Re Claim 2, Gibson as modified by Veronesi and Jha further disclose receiving, by the processing circuitry and for each geographic area of a plurality of geographic areas, a respective plurality of maps, wherein:
for each geographic area of the plurality of geographic areas, the respective plurality of maps is generated based on a respective plurality of overhead images captured during a respective portion of a time period (see Gibson: e.g., -- [0035] The machine learning system to be trained comprises a deep learning algorithm designed to semantically segment aerial or satellite images into land cover maps, which can then be processed further to output topographic maps. In this respect, the problem of creating maps from imagery treated as one of “semantic segmentation”, whereby each pixel within the image is allocated to a single class, from the set of classes that will be represented on the output land cover map, for example, building, road, water, railway, vegetation and the like. {classes read on categories} A post-processing step then generates the topographical map by “cleaning up” this output, for example, by polygonising continuous regions of pixels allocated to the same class and then regularizing those outlines into realistic shapes depending on the feature they represent, for example, so that buildings have straight edges.
[0036] It will be appreciated that any suitable machine learning model capable of classifying images may be used. For example, a convolutional neural network based on U-Net may be trained to perform semantic segmentation of aerial images using a gradient descent algorithm.
[0037] In order to train the machine learning system to accurately classify each pixel of a target image, it is important to use training data that has a balanced distribution of pixels for each of the classes that are to be used. Typically, as illustrated by FIG. 1, this is performed by capturing one or more test images 1 of different regions, and then selecting sample areas 10 (referred to herein as “patches”) within each test image 1 to label. In this respect, most test images 1 are too large to label entirely. To overcome this, each test image 1 will be divided into a grid of patches 10, and then patches 10 that represent different geographies, biomes or urban types are selected and labelled in order to obtain a set of patches, wherein each of the classes appear within at least one patch. Deciding how to select patches 10 so that all regions are represented well without giving undue preference to a given class can be very difficult to achieve and requires laborious manual inspection that is both expensive and time consuming.--, in [0002]-[0005], and [0035]-[0037];
also see Veronesi: e.g., --[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028];
further see Jha: e.g., -- a computer-implemented method for generating land use land cover (LULC) classification of a geographic area is described. The computer-implemented method comprises receiving a first input defining a geographic area and a first time frame. The computer-implemented method further comprises automatically retrieving a first set of satellite images corresponding to the geographic area and the first time frame. The computer-implemented method further comprises automatically classifying the first set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting the LULC classification of the geographic area.
[0007] According to an example, the plurality of land use land cover (LULC) classes may include at least one of vegetation cover, surface water cover, built-up area, barren/open land, and cropland.
[0008] According to an example, the computer-implemented method may further comprise creating a training set including a plurality of satellite images, and automatically training a deep learning model using the training set and a neural network to develop the trained deep learning model.
[0009] According to an example, creating a training set may further comprise automatically retrieving a plurality of satellite images corresponding to a plurality of geographic areas, automatically fetching a plurality of spectral bands corresponding to the plurality of satellite images, automatically processing the plurality of spectral bands to convert digital number of each pixel of the plurality of spectral bands into reflectance or radiance values, and creating the training set in the form of creating pixel-wise shapefiles corresponding to each of the plurality of LULC classes.--, in [0006]-[0009]; and, -- the computer-implemented method may further comprise receiving a second input defining a second time frame, automatically retrieving a second set of satellite images corresponding to the geographic area and the second time frame, automatically classifying the second set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting a comparison of the land use land cover (LULC) classes of the first and the second set of satellite images, the comparison illustrating a quantitative relative change in the land use land cover (LULC) classes of the geographic area over a time duration from the first time frame to the second time frame.
[0017] According to another exemplary embodiment, a system for generating land use land cover (LULC) classification of a geographic area is described. The system comprises at least one processor and at least one computer readable memory coupled to the at least one processor, and the processor is configured to perform all or some steps of the method described above.
[0018] According to another exemplary embodiment, a non-transitory computer readable medium is described. The non-transitory computer readable medium comprises a computer-readable code comprising instructions, which when executed by a processor, causes the processor to perform all or some steps of the method described above.
[0019] It is an object of the invention to provide a Geo-spatial artificial intelligence (Geo-AI) based fully automated computer-based method and system for predicting land use land cover (LULC) classification of a geographic area using a trained deep learning model--, in [0016]-[0019]);
the first machine learning model outputs a designation and likelihood of each pixel of each respective plurality of overhead images as being of a particular mapping category of a plurality of mapping categories (see Gibson: e.g., -- [0035] The machine learning system to be trained comprises a deep learning algorithm designed to semantically segment aerial or satellite images into land cover maps, which can then be processed further to output topographic maps. In this respect, the problem of creating maps from imagery treated as one of “semantic segmentation”, whereby each pixel within the image is allocated to a single class, from the set of classes that will be represented on the output land cover map, for example, building, road, water, railway, vegetation and the like. {classes read on categories} A post-processing step then generates the topographical map by “cleaning up” this output, for example, by polygonising continuous regions of pixels allocated to the same class and then regularizing those outlines into realistic shapes depending on the feature they represent, for example, so that buildings have straight edges.
[0036] It will be appreciated that any suitable machine learning model capable of classifying images may be used. For example, a convolutional neural network based on U-Net may be trained to perform semantic segmentation of aerial images using a gradient descent algorithm.
[0037] In order to train the machine learning system to accurately classify each pixel of a target image, it is important to use training data that has a balanced distribution of pixels for each of the classes that are to be used. Typically, as illustrated by FIG. 1, this is performed by capturing one or more test images 1 of different regions, and then selecting sample areas 10 (referred to herein as “patches”) within each test image 1 to label. In this respect, most test images 1 are too large to label entirely. To overcome this, each test image 1 will be divided into a grid of patches 10, and then patches 10 that represent different geographies, biomes or urban types are selected and labelled in order to obtain a set of patches, wherein each of the classes appear within at least one patch. Deciding how to select patches 10 so that all regions are represented well without giving undue preference to a given class can be very difficult to achieve and requires laborious manual inspection that is both expensive and time consuming.--, in [0002]-[0005], and [0035]-[0037];
also see Veronesi: e.g., --[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]);
wherein training the machine learning model is further performed using based on the pixel designations and likelihoods output by the first machine learning in association with the respective pluralities of maps for the plurality of geographic areas, to identify an expected distribution and temporal patterns for mapping categories of a particular geographic area of the plurality of geographic areas at a particular time (see Gibson: e.g., -- [0035] The machine learning system to be trained comprises a deep learning algorithm designed to semantically segment aerial or satellite images into land cover maps, which can then be processed further to output topographic maps. In this respect, the problem of creating maps from imagery treated as one of “semantic segmentation”, whereby each pixel within the image is allocated to a single class, from the set of classes that will be represented on the output land cover map, for example, building, road, water, railway, vegetation and the like. {classes read on categories} A post-processing step then generates the topographical map by “cleaning up” this output, for example, by polygonising continuous regions of pixels allocated to the same class and then regularizing those outlines into realistic shapes depending on the feature they represent, for example, so that buildings have straight edges.
[0036] It will be appreciated that any suitable machine learning model capable of classifying images may be used. For example, a convolutional neural network based on U-Net may be trained to perform semantic segmentation of aerial images using a gradient descent algorithm.
[0037] In order to train the machine learning system to accurately classify each pixel of a target image, it is important to use training data that has a balanced distribution of pixels for each of the classes that are to be used. Typically, as illustrated by FIG. 1, this is performed by capturing one or more test images 1 of different regions, and then selecting sample areas 10 (referred to herein as “patches”) within each test image 1 to label. In this respect, most test images 1 are too large to label entirely. To overcome this, each test image 1 will be divided into a grid of patches 10, and then patches 10 that represent different geographies, biomes or urban types are selected and labelled in order to obtain a set of patches, wherein each of the classes appear within at least one patch. Deciding how to select patches 10 so that all regions are represented well without giving undue preference to a given class can be very difficult to achieve and requires laborious manual inspection that is both expensive and time consuming.--, in [0002]-[0005], and [0035]-[0037];
also see Veronesi: e.g., --[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]).
Re Claim 3, Veronesi as modified by Jha and Gibson further disclose wherein training the second machine learning model causes the machine learning model to learn a temporal distribution of mapping categories and confidences observed for the geographic area over the time period (see Veronesi: e.g., --[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028];
further see Jha: e.g., -- a computer-implemented method for generating land use land cover (LULC) classification of a geographic area is described. The computer-implemented method comprises receiving a first input defining a geographic area and a first time frame. The computer-implemented method further comprises automatically retrieving a first set of satellite images corresponding to the geographic area and the first time frame. The computer-implemented method further comprises automatically classifying the first set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting the LULC classification of the geographic area.
[0007] According to an example, the plurality of land use land cover (LULC) classes may include at least one of vegetation cover, surface water cover, built-up area, barren/open land, and cropland.
[0008] According to an example, the computer-implemented method may further comprise creating a training set including a plurality of satellite images, and automatically training a deep learning model using the training set and a neural network to develop the trained deep learning model.
[0009] According to an example, creating a training set may further comprise automatically retrieving a plurality of satellite images corresponding to a plurality of geographic areas, automatically fetching a plurality of spectral bands corresponding to the plurality of satellite images, automatically processing the plurality of spectral bands to convert digital number of each pixel of the plurality of spectral bands into reflectance or radiance values, and creating the training set in the form of creating pixel-wise shapefiles corresponding to each of the plurality of LULC classes.--, in [0006]-[0009]; and, -- the computer-implemented method may further comprise receiving a second input defining a second time frame, automatically retrieving a second set of satellite images corresponding to the geographic area and the second time frame, automatically classifying the second set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting a comparison of the land use land cover (LULC) classes of the first and the second set of satellite images, the comparison illustrating a quantitative relative change in the land use land cover (LULC) classes of the geographic area over a time duration from the first time frame to the second time frame.
[0017] According to another exemplary embodiment, a system for generating land use land cover (LULC) classification of a geographic area is described. The system comprises at least one processor and at least one computer readable memory coupled to the at least one processor, and the processor is configured to perform all or some steps of the method described above.
[0018] According to another exemplary embodiment, a non-transitory computer readable medium is described. The non-transitory computer readable medium comprises a computer-readable code comprising instructions, which when executed by a processor, causes the processor to perform all or some steps of the method described above.
[0019] It is an object of the invention to provide a Geo-spatial artificial intelligence (Geo-AI) based fully automated computer-based method and system for predicting land use land cover (LULC) classification of a geographic area using a trained deep learning model--, in [0016]-[0019]); and
the method further comprises generating the model, a vector, based on the temporal distribution of mapping categories and confidences observed for geographic area over the time period, summarizing variability of mapping categories of the geographic area over the time period (see Gibson: e.g., -- [0035] The machine learning system to be trained comprises a deep learning algorithm designed to semantically segment aerial or satellite images into land cover maps, which can then be processed further to output topographic maps. In this respect, the problem of creating maps from imagery treated as one of “semantic segmentation”, whereby each pixel within the image is allocated to a single class, from the set of classes that will be represented on the output land cover map, for example, building, road, water, railway, vegetation and the like. {classes read on categories} A post-processing step then generates the topographical map by “cleaning up” this output, for example, by polygonising continuous regions of pixels allocated to the same class and then regularizing those outlines into realistic shapes depending on the feature they represent, for example, so that buildings have straight edges.
[0036] It will be appreciated that any suitable machine learning model capable of classifying images may be used. For example, a convolutional neural network based on U-Net may be trained to perform semantic segmentation of aerial images using a gradient descent algorithm.
[0037] In order to train the machine learning system to accurately classify each pixel of a target image, it is important to use training data that has a balanced distribution of pixels for each of the classes that are to be used. Typically, as illustrated by FIG. 1, this is performed by capturing one or more test images 1 of different regions, and then selecting sample areas 10 (referred to herein as “patches”) within each test image 1 to label. In this respect, most test images 1 are too large to label entirely. To overcome this, each test image 1 will be divided into a grid of patches 10, and then patches 10 that represent different geographies, biomes or urban types are selected and labelled in order to obtain a set of patches, wherein each of the classes appear within at least one patch. Deciding how to select patches 10 so that all regions are represented well without giving undue preference to a given class can be very difficult to achieve and requires laborious manual inspection that is both expensive and time consuming.--, in [0002]-[0005], and [0035]-[0037]).
Re Claim 4, Gibson as modified by Veronesi and Jha further disclose wherein the time period corresponds to: a particular year, and each respective portion of the time period corresponds to a month of the particular year (see Veronesi: e.g., --[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028];
further see Jha: e.g., -- a computer-implemented method for generating land use land cover (LULC) classification of a geographic area is described. The computer-implemented method comprises receiving a first input defining a geographic area and a first time frame. The computer-implemented method further comprises automatically retrieving a first set of satellite images corresponding to the geographic area and the first time frame. The computer-implemented method further comprises automatically classifying the first set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting the LULC classification of the geographic area.
[0007] According to an example, the plurality of land use land cover (LULC) classes may include at least one of vegetation cover, surface water cover, built-up area, barren/open land, and cropland.
[0008] According to an example, the computer-implemented method may further comprise creating a training set including a plurality of satellite images, and automatically training a deep learning model using the training set and a neural network to develop the trained deep learning model.
[0009] According to an example, creating a training set may further comprise automatically retrieving a plurality of satellite images corresponding to a plurality of geographic areas, automatically fetching a plurality of spectral bands corresponding to the plurality of satellite images, automatically processing the plurality of spectral bands to convert digital number of each pixel of the plurality of spectral bands into reflectance or radiance values, and creating the training set in the form of creating pixel-wise shapefiles corresponding to each of the plurality of LULC classes.--, in [0006]-[0009]; and, -- the computer-implemented method may further comprise receiving a second input defining a second time frame, automatically retrieving a second set of satellite images corresponding to the geographic area and the second time frame, automatically classifying the second set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting a comparison of the land use land cover (LULC) classes of the first and the second set of satellite images, the comparison illustrating a quantitative relative change in the land use land cover (LULC) classes of the geographic area over a time duration from the first time frame to the second time frame.
[0017] According to another exemplary embodiment, a system for generating land use land cover (LULC) classification of a geographic area is described. The system comprises at least one processor and at least one computer readable memory coupled to the at least one processor, and the processor is configured to perform all or some steps of the method described above.
[0018] According to another exemplary embodiment, a non-transitory computer readable medium is described. The non-transitory computer readable medium comprises a computer-readable code comprising instructions, which when executed by a processor, causes the processor to perform all or some steps of the method described above.
[0019] It is an object of the invention to provide a Geo-spatial artificial intelligence (Geo-AI) based fully automated computer-based method and system for predicting land use land cover (LULC) classification of a geographic area using a trained deep learning model--, in [0016]-[0019]);
a plurality of seasons comprising winter, spring, summer, and autumn, and each respective portion of the time period corresponds to a respective season of the plurality of seasons; or a plurality of years, and each respective portion of the time period corresponds to a respective year of the plurality of years see Veronesi: e.g., --[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028];
further see Jha: e.g., -- a computer-implemented method for generating land use land cover (LULC) classification of a geographic area is described. The computer-implemented method comprises receiving a first input defining a geographic area and a first time frame. The computer-implemented method further comprises automatically retrieving a first set of satellite images corresponding to the geographic area and the first time frame. The computer-implemented method further comprises automatically classifying the first set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting the LULC classification of the geographic area.
[0007] According to an example, the plurality of land use land cover (LULC) classes may include at least one of vegetation cover, surface water cover, built-up area, barren/open land, and cropland.
[0008] According to an example, the computer-implemented method may further comprise creating a training set including a plurality of satellite images, and automatically training a deep learning model using the training set and a neural network to develop the trained deep learning model.
[0009] According to an example, creating a training set may further comprise automatically retrieving a plurality of satellite images corresponding to a plurality of geographic areas, automatically fetching a plurality of spectral bands corresponding to the plurality of satellite images, automatically processing the plurality of spectral bands to convert digital number of each pixel of the plurality of spectral bands into reflectance or radiance values, and creating the training set in the form of creating pixel-wise shapefiles corresponding to each of the plurality of LULC classes.--, in [0006]-[0009]; and, -- the computer-implemented method may further comprise receiving a second input defining a second time frame, automatically retrieving a second set of satellite images corresponding to the geographic area and the second time frame, automatically classifying the second set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting a comparison of the land use land cover (LULC) classes of the first and the second set of satellite images, the comparison illustrating a quantitative relative change in the land use land cover (LULC) classes of the geographic area over a time duration from the first time frame to the second time frame.
[0017] According to another exemplary embodiment, a system for generating land use land cover (LULC) classification of a geographic area is described. The system comprises at least one processor and at least one computer readable memory coupled to the at least one processor, and the processor is configured to perform all or some steps of the method described above.
[0018] According to another exemplary embodiment, a non-transitory computer readable medium is described. The non-transitory computer readable medium comprises a computer-readable code comprising instructions, which when executed by a processor, causes the processor to perform all or some steps of the method described above.
[0019] It is an object of the invention to provide a Geo-spatial artificial intelligence (Geo-AI) based fully automated computer-based method and system for predicting land use land cover (LULC) classification of a geographic area using a trained deep learning model--, in [0016]-[0019]).
Re Claim 5, Gibson as modified by Veronesi and Jha further disclose wherein the second machine learning model is a statistical machine learning model, a Bayesian probabilistic model, a random forest model, a deep learning model, a transformer model using attention heads, or a combination thereof (see Gibson: e.g., --[0035] The machine learning system to be trained comprises a deep learning algorithm designed to semantically segment aerial or satellite images into land cover maps, which can then be processed further to output topographic maps. In this respect, the problem of creating maps from imagery treated as one of “semantic segmentation”, whereby each pixel within the image is allocated to a single class, from the set of classes that will be represented on the output land cover map, for example, building, road, water, railway, vegetation and the like. A post-processing step then generates the topographical map by “cleaning up” this output, for example, by polygonising continuous regions of pixels allocated to the same class and then regularizing those outlines into realistic shapes depending on the feature they represent, for example, so that buildings have straight edges.
[0036] It will be appreciated that any suitable machine learning model capable of classifying images may be used. For example, a convolutional neural network based on U-Net may be trained to perform semantic segmentation of aerial images using a gradient descent algorithm.
[0037] In order to train the machine learning system to accurately classify each pixel of a target image, it is important to use training data that has a balanced distribution of pixels for each of the classes that are to be used. Typically, as illustrated by FIG. 1, this is performed by capturing one or more test images 1 of different regions, and then selecting sample areas 10 (referred to herein as “patches”) within each test image 1 to label. In this respect, most test images 1 are too large to label entirely. To overcome this, each test image 1 will be divided into a grid of patches 10, and then patches 10 that represent different geographies, biomes or urban types are selected and labelled in order to obtain a set of patches, wherein each of the classes appear within at least one patch. Deciding how to select patches 10 so that all regions are represented well without giving undue preference to a given class can be very difficult to achieve and requires laborious manual inspection that is both expensive and time consuming.
[0038] Aspects described herein seek to improve the generation of training data by providing methods of optimizing the distribution of sample areas, wherein the patches are selected by both region and class, and methods of optimizing the patch selection to minimize class imbalance within each patch.
Distribution of Patches [0039] A problem with geographic distribution of training data is that examples of some classes only appear in certain areas. For example, railway lines will not appear in a large number of images, compared to buildings or vegetation. A random distribution actively biases against these classes and would result in a small number of patches being used to determine an entire class.
[0040] Additionally, different areas have different signatures (for example, buildings in the business district of a city will look different to those in a residential area), and so it is important to select patches from different locations across a wide variety of different classes, as patches from the same area tend to train the machine learning system to only detect features from that location.
[0041] The distribution method described herein thus ensures that the patches fed into the model have at least some of each classes in equal proportion, and ensures that the existence of the class is balanced across the set of patches. This helps to smooth and stabilize the minority classes and ensures the model actively trains on the class across thousands of examples rather than a few hundred.--, in [0035]-[0041], and
--[0045] For example, for a dataset of 10000 patches and 5 classes, it may be necessary that each class exists in at least 2000 of the patches. This means that the model is exposed to the class during gradient descent even if only in a few pixels. Any shortfalls due to saturation can be made up, for example, using data augmentation techniques, which will be targeted at the relevant class rather than being random. Likewise, if that dataset of 10000 patches corresponds to 100 regions, approximately 100 patches will have been taken from each region.
[0046] In known methods, random sampling is used to tile up an image into patches, and this often biases against certain classes, leaving only a hundred patches to train from out of a dataset that may comprise 10000 patches.
[0047] Therefore, an objective is to determine a distribution within the training dataset that best represents the geographic diversity but also enforces a minimum frequency with which classes will appear within the patch dataset.--, in [0045]-[0047];
also see Veronesi: e.g., --[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028];
further see Jha: e.g., -- a computer-implemented method for generating land use land cover (LULC) classification of a geographic area is described. The computer-implemented method comprises receiving a first input defining a geographic area and a first time frame. The computer-implemented method further comprises automatically retrieving a first set of satellite images corresponding to the geographic area and the first time frame. The computer-implemented method further comprises automatically classifying the first set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting the LULC classification of the geographic area.
[0007] According to an example, the plurality of land use land cover (LULC) classes may include at least one of vegetation cover, surface water cover, built-up area, barren/open land, and cropland.
[0008] According to an example, the computer-implemented method may further comprise creating a training set including a plurality of satellite images, and automatically training a deep learning model using the training set and a neural network to develop the trained deep learning model.
[0009] According to an example, creating a training set may further comprise automatically retrieving a plurality of satellite images corresponding to a plurality of geographic areas, automatically fetching a plurality of spectral bands corresponding to the plurality of satellite images, automatically processing the plurality of spectral bands to convert digital number of each pixel of the plurality of spectral bands into reflectance or radiance values, and creating the training set in the form of creating pixel-wise shapefiles corresponding to each of the plurality of LULC classes.--, in [0006]-[0009]; and, -- the computer-implemented method may further comprise receiving a second input defining a second time frame, automatically retrieving a second set of satellite images corresponding to the geographic area and the second time frame, automatically classifying the second set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting a comparison of the land use land cover (LULC) classes of the first and the second set of satellite images, the comparison illustrating a quantitative relative change in the land use land cover (LULC) classes of the geographic area over a time duration from the first time frame to the second time frame.
[0017] According to another exemplary embodiment, a system for generating land use land cover (LULC) classification of a geographic area is described. The system comprises at least one processor and at least one computer readable memory coupled to the at least one processor, and the processor is configured to perform all or some steps of the method described above.
[0018] According to another exemplary embodiment, a non-transitory computer readable medium is described. The non-transitory computer readable medium comprises a computer-readable code comprising instructions, which when executed by a processor, causes the processor to perform all or some steps of the method described above.
[0019] It is an object of the invention to provide a Geo-spatial artificial intelligence (Geo-AI) based fully automated computer-based method and system for predicting land use land cover (LULC) classification of a geographic area using a trained deep learning model--, in [0016]-[0019]);
.
Re Claim 6, Gibson as modified by Veronesi and Jha further disclose wherein training the second machine learning model further comprises: using auxiliary geospatial data as a parameter during training of the machine learning wherein the auxiliary geospatial data comprises at least one of weather data, topographic data, or signal emissions data for the geographic area (see Gibson: e.g., -- computer-implemented methods and systems are provided for pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system. To do this, the amount that each class is contained within each area image relative to the whole dataset is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken-, in abstract, and, -- computer-implemented methods and systems for generating training data for training a machine learning system to automatically extract topographic features. In particular, aspects described herein relate to a method of pre-processing image data to generate improved training data.--, in [0001], and, -- pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system, with each image containing a part of the geographic region of interest. To do this, the amount that each class is contained within each area image relative to the whole dataset (i.e., the whole geographic region captured) is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken. This ensures that the distribution of patches taken from each area prioritises features that are less common….. a computer-implemented method of processing image data for use in training a machine learning system for classifying image data, the method comprising obtaining image data comprising a plurality of images, each image corresponding to a respective geographic area, processing the image data to identify one or more classes of topographic feature contained within each image, wherein processing the image data comprises determining a quantity of each respective topographic feature class contained within each image, generating a first dataset comprising a set of values for each image, wherein each value is representative of the quantity of one of the plurality of topographic feature classes contained within the respective image, and processing the first dataset to determine a number of samples required from each image for each topographic feature class. Processing the first dataset comprises (i) generating a normalised dataset, wherein the values of the first dataset are normalised across each image and each topographic feature class, and (ii) for each topographic feature class, calculating the number of samples required from each image based on the normalised dataset and a target number of samples for the respective topographic feature class. --, in [0008]-[0010];
also see Veronesi: e.g., --the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]).
Re Claim 7, Gibson as modified by Veronesi and Jha further disclose determining one or more trends for the mapping categories of the geographic area over the time period (see Veronesi: e.g., --the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]),
wherein training the second machine learning model is further based on the determined one or more trends (see Veronesi: e.g., --the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]).
Re Claim 8, Gibson as modified by Veronesi and Jha further disclose wherein training the second machine learning model further comprises: determining a set of hyperparameters of the second machine learning model based on identifying spatial segments and temporal segments summarizing the respective pluralities of overhead image (see Gibson: e.g., -- [0035] The machine learning system to be trained comprises a deep learning algorithm designed to semantically segment aerial or satellite images into land cover maps, which can then be processed further to output topographic maps. In this respect, the problem of creating maps from imagery treated as one of “semantic segmentation”, whereby each pixel within the image is allocated to a single class, from the set of classes that will be represented on the output land cover map, for example, building, road, water, railway, vegetation and the like. {classes read on categories} A post-processing step then generates the topographical map by “cleaning up” this output, for example, by polygonising continuous regions of pixels allocated to the same class and then regularizing those outlines into realistic shapes depending on the feature they represent, for example, so that buildings have straight edges.
[0036] It will be appreciated that any suitable machine learning model capable of classifying images may be used. For example, a convolutional neural network based on U-Net may be trained to perform semantic segmentation of aerial images using a gradient descent algorithm.
[0037] In order to train the machine learning system to accurately classify each pixel of a target image, it is important to use training data that has a balanced distribution of pixels for each of the classes that are to be used. Typically, as illustrated by FIG. 1, this is performed by capturing one or more test images 1 of different regions, and then selecting sample areas 10 (referred to herein as “patches”) within each test image 1 to label. In this respect, most test images 1 are too large to label entirely. To overcome this, each test image 1 will be divided into a grid of patches 10, and then patches 10 that represent different geographies, biomes or urban types are selected and labelled in order to obtain a set of patches, wherein each of the classes appear within at least one patch. Deciding how to select patches 10 so that all regions are represented well without giving undue preference to a given class can be very difficult to achieve and requires laborious manual inspection that is both expensive and time consuming.--, in [0002]-[0005], and [0035]-[0037]; also see Veronesi: e.g., --the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]).
Re Claim 15, Gibson discloses a method comprising: identifying a distribution of mapping categories for a geographic area, based on at least one overhead image of the geographic area (see Gibson: e.g., -- computer-implemented methods and systems are provided for pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system. To do this, the amount that each class is contained within each area image relative to the whole dataset is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken-, in abstract, and, -- computer-implemented methods and systems for generating training data for training a machine learning system to automatically extract topographic features. In particular, aspects described herein relate to a method of pre-processing image data to generate improved training data.--, in [0001], and, -- pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system, with each image containing a part of the geographic region of interest. To do this, the amount that each class is contained within each area image relative to the whole dataset (i.e., the whole geographic region captured) is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken. This ensures that the distribution of patches taken from each area prioritises features that are less common….. a computer-implemented method of processing image data for use in training a machine learning system for classifying image data, the method comprising obtaining image data comprising a plurality of images, each image corresponding to a respective geographic area, processing the image data to identify one or more classes of topographic feature contained within each image, wherein processing the image data comprises determining a quantity of each respective topographic feature class contained within each image, generating a first dataset comprising a set of values for each image, wherein each value is representative of the quantity of one of the plurality of topographic feature classes contained within the respective image, and processing the first dataset to determine a number of samples required from each image for each topographic feature class. Processing the first dataset comprises (i) generating a normalised dataset, wherein the values of the first dataset are normalised across each image and each topographic feature class, and (ii) for each topographic feature class, calculating the number of samples required from each image based on the normalised dataset and a target number of samples for the respective topographic feature class. --, in [0008]-[0010]);
Gibson however does not explicitly disclose overhead image of the geographic area captured at a given time,
Veronesi discloses overhead image of the geographic area captured at a given time (see Veronesi: e.g., --the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]);
Veronesi and Gibson are combinable as they are in the same field of endeavor: machine learning algorithms used in geographic mapping and images processing. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Gibson’s method using Veronesi’s teachings by including overhead image of the geographic area captured at a given time to Gibson’ geographic mapping in order to apply a machine learning model using the feature vector to generate geographic mapping for each of the plurality of pixels of the images for the right time and high accuracy (see Veronesi: e.g., in abstract, [00003]-[0005], [0021], [0025]-[0028], and [0058]);
Gibson as modified by Veronesi and Jha further disclose inputting indications of the given time and the geographic area to a second trained machine learning model (see Gibson: e.g., -- [0035] The machine learning system to be trained comprises a deep learning algorithm designed to semantically segment aerial or satellite images into land cover maps, which can then be processed further to output topographic maps. In this respect, the problem of creating maps from imagery treated as one of “semantic segmentation”, whereby each pixel within the image is allocated to a single class, from the set of classes that will be represented on the output land cover map, for example, building, road, water, railway, vegetation and the like. {classes read on categories} A post-processing step then generates the topographical map by “cleaning up” this output, for example, by polygonising continuous regions of pixels allocated to the same class and then regularizing those outlines into realistic shapes depending on the feature they represent, for example, so that buildings have straight edges.
[0036] It will be appreciated that any suitable machine learning model capable of classifying images may be used. For example, a convolutional neural network based on U-Net may be trained to perform semantic segmentation of aerial images using a gradient descent algorithm.
[0037] In order to train the machine learning system to accurately classify each pixel of a target image, it is important to use training data that has a balanced distribution of pixels for each of the classes that are to be used. Typically, as illustrated by FIG. 1, this is performed by capturing one or more test images 1 of different regions, and then selecting sample areas 10 (referred to herein as “patches”) within each test image 1 to label. In this respect, most test images 1 are too large to label entirely. To overcome this, each test image 1 will be divided into a grid of patches 10, and then patches 10 that represent different geographies, biomes or urban types are selected and labelled in order to obtain a set of patches, wherein each of the classes appear within at least one patch. Deciding how to select patches 10 so that all regions are represented well without giving undue preference to a given class can be very difficult to achieve and requires laborious manual inspection that is both expensive and time consuming.--, in [0002]-[0005], and [0035]-[0037]; also see Veronesi: e.g., --the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]);
wherein: the second trained machine learning model is obtained based on pixel designations and likelihoods output by a first trained machine learning model in association with a plurality of maps of the geographic area (see Veronesi: e.g., --receiving a point cloud of a digital surface model of the wooded area; concatenating data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in abstract;
--the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028];
{apparently, Veronesi’s machine learning model as the second machine learning model is different from Gibson’s machine learning model as the first learning model, at least Veronesi’s learning model takes input including 1) labeled data, feature vectors, and topographic variables of geographic regions {with time, seasonal changes}, and output as “to generate timber data for each of the plurality of pixels of the image; and generating an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image”, which read on claimed output of the second learning model as “to identify the expected distribution and temporal patterns for the mapping categories of the geographic area at the given time”});
each map of the plurality of maps is generated based on a respective plurality of overhead images captured during a respective portion of a time period, each overhead image of the respective pluralities of overhead images comprises a respective plurality of pixels, and the pixel designations and likelihoods output by first trained machine learning model indicate each pixel as being of a particular mapping category of a plurality of mapping categories (see Gibson: e.g., -- [0035] The machine learning system to be trained comprises a deep learning algorithm designed to semantically segment aerial or satellite images into land cover maps, which can then be processed further to output topographic maps. In this respect, the problem of creating maps from imagery treated as one of “semantic segmentation”, whereby each pixel within the image is allocated to a single class, from the set of classes that will be represented on the output land cover map, for example, building, road, water, railway, vegetation and the like. {classes read on categories} A post-processing step then generates the topographical map by “cleaning up” this output, for example, by polygonising continuous regions of pixels allocated to the same class and then regularizing those outlines into realistic shapes depending on the feature they represent, for example, so that buildings have straight edges.
[0036] It will be appreciated that any suitable machine learning model capable of classifying images may be used. For example, a convolutional neural network based on U-Net may be trained to perform semantic segmentation of aerial images using a gradient descent algorithm.
[0037] In order to train the machine learning system to accurately classify each pixel of a target image, it is important to use training data that has a balanced distribution of pixels for each of the classes that are to be used. Typically, as illustrated by FIG. 1, this is performed by capturing one or more test images 1 of different regions, and then selecting sample areas 10 (referred to herein as “patches”) within each test image 1 to label. In this respect, most test images 1 are too large to label entirely. To overcome this, each test image 1 will be divided into a grid of patches 10, and then patches 10 that represent different geographies, biomes or urban types are selected and labelled in order to obtain a set of patches, wherein each of the classes appear within at least one patch. Deciding how to select patches 10 so that all regions are represented well without giving undue preference to a given class can be very difficult to achieve and requires laborious manual inspection that is both expensive and time consuming.--, in [0002]-[0005], and [0035]-[0037]; also see Veronesi: e.g., --the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]);
outputting, using the second trained machine learning model, an expected distribution and temporal patterns of the mapping categories of the geographic area at the given time (see Veronesi: e.g., --the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]; further see Jha: e.g., -- a computer-implemented method for generating land use land cover (LULC) classification of a geographic area is described. The computer-implemented method comprises receiving a first input defining a geographic area and a first time frame. The computer-implemented method further comprises automatically retrieving a first set of satellite images corresponding to the geographic area and the first time frame. The computer-implemented method further comprises automatically classifying the first set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting the LULC classification of the geographic area.
[0007] According to an example, the plurality of land use land cover (LULC) classes may include at least one of vegetation cover, surface water cover, built-up area, barren/open land, and cropland.
[0008] According to an example, the computer-implemented method may further comprise creating a training set including a plurality of satellite images, and automatically training a deep learning model using the training set and a neural network to develop the trained deep learning model.
[0009] According to an example, creating a training set may further comprise automatically retrieving a plurality of satellite images corresponding to a plurality of geographic areas, automatically fetching a plurality of spectral bands corresponding to the plurality of satellite images, automatically processing the plurality of spectral bands to convert digital number of each pixel of the plurality of spectral bands into reflectance or radiance values, and creating the training set in the form of creating pixel-wise shapefiles corresponding to each of the plurality of LULC classes.--, in [0006]-[0009]; and, -- the computer-implemented method may further comprise receiving a second input defining a second time frame, automatically retrieving a second set of satellite images corresponding to the geographic area and the second time frame, automatically classifying the second set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting a comparison of the land use land cover (LULC) classes of the first and the second set of satellite images, the comparison illustrating a quantitative relative change in the land use land cover (LULC) classes of the geographic area over a time duration from the first time frame to the second time frame.
[0017] According to another exemplary embodiment, a system for generating land use land cover (LULC) classification of a geographic area is described. The system comprises at least one processor and at least one computer readable memory coupled to the at least one processor, and the processor is configured to perform all or some steps of the method described above.
[0018] According to another exemplary embodiment, a non-transitory computer readable medium is described. The non-transitory computer readable medium comprises a computer-readable code comprising instructions, which when executed by a processor, causes the processor to perform all or some steps of the method described above.
[0019] It is an object of the invention to provide a Geo-spatial artificial intelligence (Geo-AI) based fully automated computer-based method and system for predicting land use land cover (LULC) classification of a geographic area using a trained deep learning model--, in [0016]-[0019]);
comparing the identified distribution of mapping categories for the geographic area to the expected distribution of the mapping categories of the geographic area at the given time to identify difference between the identified distribution and the expected distribution (see Veronesi: e.g., --the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]; further see Jha: e.g., -- a computer-implemented method for generating land use land cover (LULC) classification of a geographic area is described. The computer-implemented method comprises receiving a first input defining a geographic area and a first time frame. The computer-implemented method further comprises automatically retrieving a first set of satellite images corresponding to the geographic area and the first time frame. The computer-implemented method further comprises automatically classifying the first set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting the LULC classification of the geographic area.
[0007] According to an example, the plurality of land use land cover (LULC) classes may include at least one of vegetation cover, surface water cover, built-up area, barren/open land, and cropland.
[0008] According to an example, the computer-implemented method may further comprise creating a training set including a plurality of satellite images, and automatically training a deep learning model using the training set and a neural network to develop the trained deep learning model.
[0009] According to an example, creating a training set may further comprise automatically retrieving a plurality of satellite images corresponding to a plurality of geographic areas, automatically fetching a plurality of spectral bands corresponding to the plurality of satellite images, automatically processing the plurality of spectral bands to convert digital number of each pixel of the plurality of spectral bands into reflectance or radiance values, and creating the training set in the form of creating pixel-wise shapefiles corresponding to each of the plurality of LULC classes.--, in [0006]-[0009]; and, -- the computer-implemented method may further comprise receiving a second input defining a second time frame, automatically retrieving a second set of satellite images corresponding to the geographic area and the second time frame, automatically classifying the second set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting a comparison of the land use land cover (LULC) classes of the first and the second set of satellite images, the comparison illustrating a quantitative relative change in the land use land cover (LULC) classes of the geographic area over a time duration from the first time frame to the second time frame.
[0017] According to another exemplary embodiment, a system for generating land use land cover (LULC) classification of a geographic area is described. The system comprises at least one processor and at least one computer readable memory coupled to the at least one processor, and the processor is configured to perform all or some steps of the method described above.
[0018] According to another exemplary embodiment, a non-transitory computer readable medium is described. The non-transitory computer readable medium comprises a computer-readable code comprising instructions, which when executed by a processor, causes the processor to perform all or some steps of the method described above.
[0019] It is an object of the invention to provide a Geo-spatial artificial intelligence (Geo-AI) based fully automated computer-based method and system for predicting land use land cover (LULC) classification of a geographic area using a trained deep learning model--, in [0016]-[0019]); and
determining whether the difference between the identified distribution and the expected distribution is an anomaly (see Gibson: e.g., -- computer-implemented methods and systems are provided for pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system. To do this, the amount that each class is contained within each area image relative to the whole dataset is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken-, in abstract, and, -- computer-implemented methods and systems for generating training data for training a machine learning system to automatically extract topographic features. In particular, aspects described herein relate to a method of pre-processing image data to generate improved training data.--, in [0001], and, -- pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system, with each image containing a part of the geographic region of interest. To do this, the amount that each class is contained within each area image relative to the whole dataset (i.e., the whole geographic region captured) is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken. This ensures that the distribution of patches taken from each area prioritises features that are less common….. a computer-implemented method of processing image data for use in training a machine learning system for classifying image data, the method comprising obtaining image data comprising a plurality of images, each image corresponding to a respective geographic area, processing the image data to identify one or more classes of topographic feature contained within each image, wherein processing the image data comprises determining a quantity of each respective topographic feature class contained within each image, generating a first dataset comprising a set of values for each image, wherein each value is representative of the quantity of one of the plurality of topographic feature classes contained within the respective image, and processing the first dataset to determine a number of samples required from each image for each topographic feature class. Processing the first dataset comprises (i) generating a normalised dataset, wherein the values of the first dataset are normalised across each image and each topographic feature class, and (ii) for each topographic feature class, calculating the number of samples required from each image based on the normalised dataset and a target number of samples for the respective topographic feature class. --, in [0008]-[0010]).
Re Claim 17, Gibson as modified by Veronesi and Jha further disclose the comparing comprises identifying a plurality of candidate differences between the identified distribution and the expected distribution (see Gibson: e.g., -- computer-implemented methods and systems are provided for pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system. To do this, the amount that each class is contained within each area image relative to the whole dataset is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken-, in abstract, and, -- computer-implemented methods and systems for generating training data for training a machine learning system to automatically extract topographic features. In particular, aspects described herein relate to a method of pre-processing image data to generate improved training data.--, in [0001], and, -- pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system, with each image containing a part of the geographic region of interest. To do this, the amount that each class is contained within each area image relative to the whole dataset (i.e., the whole geographic region captured) is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken. This ensures that the distribution of patches taken from each area prioritises features that are less common….. a computer-implemented method of processing image data for use in training a machine learning system for classifying image data, the method comprising obtaining image data comprising a plurality of images, each image corresponding to a respective geographic area, processing the image data to identify one or more classes of topographic feature contained within each image, wherein processing the image data comprises determining a quantity of each respective topographic feature class contained within each image, generating a first dataset comprising a set of values for each image, wherein each value is representative of the quantity of one of the plurality of topographic feature classes contained within the respective image, and processing the first dataset to determine a number of samples required from each image for each topographic feature class. Processing the first dataset comprises (i) generating a normalised dataset, wherein the values of the first dataset are normalised across each image and each topographic feature class, and (ii) for each topographic feature class, calculating the number of samples required from each image based on the normalised dataset and a target number of samples for the respective topographic feature class. --, in [0008]-[0010]); and
determining whether the difference between the identified distribution and the expected distribution is an anomaly comprises ranking the plurality of candidate differences based on a degree to which a respective candidate difference of the plurality of candidate differences differs from the expected distribution (see Gibson: e.g., -- computer-implemented methods and systems are provided for pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system. To do this, the amount that each class is contained within each area image relative to the whole dataset is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken-, in abstract, and, -- computer-implemented methods and systems for generating training data for training a machine learning system to automatically extract topographic features. In particular, aspects described herein relate to a method of pre-processing image data to generate improved training data.--, in [0001], and, -- pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system, with each image containing a part of the geographic region of interest. To do this, the amount that each class is contained within each area image relative to the whole dataset (i.e., the whole geographic region captured) is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken. This ensures that the distribution of patches taken from each area prioritises features that are less common….. a computer-implemented method of processing image data for use in training a machine learning system for classifying image data, the method comprising obtaining image data comprising a plurality of images, each image corresponding to a respective geographic area, processing the image data to identify one or more classes of topographic feature contained within each image, wherein processing the image data comprises determining a quantity of each respective topographic feature class contained within each image, generating a first dataset comprising a set of values for each image, wherein each value is representative of the quantity of one of the plurality of topographic feature classes contained within the respective image, and processing the first dataset to determine a number of samples required from each image for each topographic feature class. Processing the first dataset comprises (i) generating a normalised dataset, wherein the values of the first dataset are normalised across each image and each topographic feature class, and (ii) for each topographic feature class, calculating the number of samples required from each image based on the normalised dataset and a target number of samples for the respective topographic feature class. --, in [0008]-[0010]).
Re Claim 18, Gibson as modified by Veronesi and Jha further disclose wherein the ranking further comprises: categorizing each respective candidate difference of the plurality of candidate differences based on a likelihood that the respective candidate difference is an anomaly, each categorization including an indication of an observed feature related to whether the respective candidate difference is an anomaly (see Veronesi: e.g., --the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]; further see Jha: e.g., -- a computer-implemented method for generating land use land cover (LULC) classification of a geographic area is described. The computer-implemented method comprises receiving a first input defining a geographic area and a first time frame. The computer-implemented method further comprises automatically retrieving a first set of satellite images corresponding to the geographic area and the first time frame. The computer-implemented method further comprises automatically classifying the first set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting the LULC classification of the geographic area.
[0007] According to an example, the plurality of land use land cover (LULC) classes may include at least one of vegetation cover, surface water cover, built-up area, barren/open land, and cropland.
[0008] According to an example, the computer-implemented method may further comprise creating a training set including a plurality of satellite images, and automatically training a deep learning model using the training set and a neural network to develop the trained deep learning model.
[0009] According to an example, creating a training set may further comprise automatically retrieving a plurality of satellite images corresponding to a plurality of geographic areas, automatically fetching a plurality of spectral bands corresponding to the plurality of satellite images, automatically processing the plurality of spectral bands to convert digital number of each pixel of the plurality of spectral bands into reflectance or radiance values, and creating the training set in the form of creating pixel-wise shapefiles corresponding to each of the plurality of LULC classes.--, in [0006]-[0009]; and, -- the computer-implemented method may further comprise receiving a second input defining a second time frame, automatically retrieving a second set of satellite images corresponding to the geographic area and the second time frame, automatically classifying the second set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting a comparison of the land use land cover (LULC) classes of the first and the second set of satellite images, the comparison illustrating a quantitative relative change in the land use land cover (LULC) classes of the geographic area over a time duration from the first time frame to the second time frame.
[0017] According to another exemplary embodiment, a system for generating land use land cover (LULC) classification of a geographic area is described. The system comprises at least one processor and at least one computer readable memory coupled to the at least one processor, and the processor is configured to perform all or some steps of the method described above.
[0018] According to another exemplary embodiment, a non-transitory computer readable medium is described. The non-transitory computer readable medium comprises a computer-readable code comprising instructions, which when executed by a processor, causes the processor to perform all or some steps of the method described above.
[0019] It is an object of the invention to provide a Geo-spatial artificial intelligence (Geo-AI) based fully automated computer-based method and system for predicting land use land cover (LULC) classification of a geographic area using a trained deep learning model--, in [0016]-[0019]).
Re Claim 19, Gibson as modified by Veronesi and Jha further disclose storing the plurality of candidate differences, and each respective categorization, in an unstructured database (see Veronesi: e.g., --[0027] Among the additional geospatial data, the system extracts elevation, slope, and aspect from databases such as the United States Geological Service (USGS) National Elevation Dataset and climate data (precipitation, temperature, and solar radiation) from ClimNA, which may be specific to North America. Soil data may also be included in the modeling from databases such as the gNATSGO database. Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.--, in [0027], and, -- [0033] Memory 214 may include a data collector 216, a data pre-processor 218, a feature vector generator 220, a machine learning model 222, a model trainer 224, a data post-processor 226, an overlay generator 228, and a normalization database 230. In brief overview, components 216-230 may cooperate to collect different types of data and images of a geographical region. Components 216-230 may generate a feature vector from data and the images and input the feature vector into a machine learning model that has been trained to output timber data for individual pixels of images. The machine learning model may output timber data for the image and components 216-230 may generate an interactive overlay from the timber data for display on a graphical user interface (GUI) 232.--, in [0031]-[0033], and, -- Data collector 216 may store the sets of measurements in a database (not shown) within forest inventory manager 206 to be used as labels in training datasets.--, in [0043]-[0045]).
Re Claim 20, Gibson as modified by Veronesi and Jha further disclose wherein identifying the difference between the identified distribution and the expected distribution further comprises: applying spatial clustering to segment spatially adjacent pixels of the at least one overhead image, likely to correspond to the difference between the identified distribution and the expected distribution, into an entity described by a vector (see Veronesi: e.g., --the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]);
Re Claim 21, Gibson as modified by Veronesi and Jha further disclose based on whether the difference is determined to be an anomaly, recommending performance of, or automatically performing, a follow-up observation of the geographic area (see Veronesi: e.g., --the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]; further see Jha: e.g., -- a computer-implemented method for generating land use land cover (LULC) classification of a geographic area is described. The computer-implemented method comprises receiving a first input defining a geographic area and a first time frame. The computer-implemented method further comprises automatically retrieving a first set of satellite images corresponding to the geographic area and the first time frame. The computer-implemented method further comprises automatically classifying the first set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting the LULC classification of the geographic area.
[0007] According to an example, the plurality of land use land cover (LULC) classes may include at least one of vegetation cover, surface water cover, built-up area, barren/open land, and cropland.
[0008] According to an example, the computer-implemented method may further comprise creating a training set including a plurality of satellite images, and automatically training a deep learning model using the training set and a neural network to develop the trained deep learning model.
[0009] According to an example, creating a training set may further comprise automatically retrieving a plurality of satellite images corresponding to a plurality of geographic areas, automatically fetching a plurality of spectral bands corresponding to the plurality of satellite images, automatically processing the plurality of spectral bands to convert digital number of each pixel of the plurality of spectral bands into reflectance or radiance values, and creating the training set in the form of creating pixel-wise shapefiles corresponding to each of the plurality of LULC classes.--, in [0006]-[0009]; and, -- the computer-implemented method may further comprise receiving a second input defining a second time frame, automatically retrieving a second set of satellite images corresponding to the geographic area and the second time frame, automatically classifying the second set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting a comparison of the land use land cover (LULC) classes of the first and the second set of satellite images, the comparison illustrating a quantitative relative change in the land use land cover (LULC) classes of the geographic area over a time duration from the first time frame to the second time frame.
[0017] According to another exemplary embodiment, a system for generating land use land cover (LULC) classification of a geographic area is described. The system comprises at least one processor and at least one computer readable memory coupled to the at least one processor, and the processor is configured to perform all or some steps of the method described above.
[0018] According to another exemplary embodiment, a non-transitory computer readable medium is described. The non-transitory computer readable medium comprises a computer-readable code comprising instructions, which when executed by a processor, causes the processor to perform all or some steps of the method described above.
[0019] It is an object of the invention to provide a Geo-spatial artificial intelligence (Geo-AI) based fully automated computer-based method and system for predicting land use land cover (LULC) classification of a geographic area using a trained deep learning model--, in [0016]-[0019]).
Re Claim 22, Gibson as modified by Veronesi and Jha further disclose wherein the recommending of the performance of, or the automatic performance of, the follow-up observation of the geographic area is performed based on receiving user feedback accepting the difference as an anomaly (see Veronesi: e.g., --the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]; further see Jha: e.g., -- a computer-implemented method for generating land use land cover (LULC) classification of a geographic area is described. The computer-implemented method comprises receiving a first input defining a geographic area and a first time frame. The computer-implemented method further comprises automatically retrieving a first set of satellite images corresponding to the geographic area and the first time frame. The computer-implemented method further comprises automatically classifying the first set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting the LULC classification of the geographic area.
[0007] According to an example, the plurality of land use land cover (LULC) classes may include at least one of vegetation cover, surface water cover, built-up area, barren/open land, and cropland.
[0008] According to an example, the computer-implemented method may further comprise creating a training set including a plurality of satellite images, and automatically training a deep learning model using the training set and a neural network to develop the trained deep learning model.
[0009] According to an example, creating a training set may further comprise automatically retrieving a plurality of satellite images corresponding to a plurality of geographic areas, automatically fetching a plurality of spectral bands corresponding to the plurality of satellite images, automatically processing the plurality of spectral bands to convert digital number of each pixel of the plurality of spectral bands into reflectance or radiance values, and creating the training set in the form of creating pixel-wise shapefiles corresponding to each of the plurality of LULC classes.--, in [0006]-[0009]; and, -- the computer-implemented method may further comprise receiving a second input defining a second time frame, automatically retrieving a second set of satellite images corresponding to the geographic area and the second time frame, automatically classifying the second set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting a comparison of the land use land cover (LULC) classes of the first and the second set of satellite images, the comparison illustrating a quantitative relative change in the land use land cover (LULC) classes of the geographic area over a time duration from the first time frame to the second time frame.
[0017] According to another exemplary embodiment, a system for generating land use land cover (LULC) classification of a geographic area is described. The system comprises at least one processor and at least one computer readable memory coupled to the at least one processor, and the processor is configured to perform all or some steps of the method described above.
[0018] According to another exemplary embodiment, a non-transitory computer readable medium is described. The non-transitory computer readable medium comprises a computer-readable code comprising instructions, which when executed by a processor, causes the processor to perform all or some steps of the method described above.
[0019] It is an object of the invention to provide a Geo-spatial artificial intelligence (Geo-AI) based fully automated computer-based method and system for predicting land use land cover (LULC) classification of a geographic area using a trained deep learning model--, in [0016]-[0019]).
Re Claim 23, Gibson as modified by Veronesi and Jha further disclose a type of the difference comprises a first mapping category indicated in the identified distribution for a portion of the geographic area being different from a second mapping category indicated in the expected distribution for the portion of the geographic area; and the follow-up observation is identified based on the type of the difference (see Veronesi: e.g., --the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]; further see Jha: e.g., -- a computer-implemented method for generating land use land cover (LULC) classification of a geographic area is described. The computer-implemented method comprises receiving a first input defining a geographic area and a first time frame. The computer-implemented method further comprises automatically retrieving a first set of satellite images corresponding to the geographic area and the first time frame. The computer-implemented method further comprises automatically classifying the first set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting the LULC classification of the geographic area.
[0007] According to an example, the plurality of land use land cover (LULC) classes may include at least one of vegetation cover, surface water cover, built-up area, barren/open land, and cropland.
[0008] According to an example, the computer-implemented method may further comprise creating a training set including a plurality of satellite images, and automatically training a deep learning model using the training set and a neural network to develop the trained deep learning model.
[0009] According to an example, creating a training set may further comprise automatically retrieving a plurality of satellite images corresponding to a plurality of geographic areas, automatically fetching a plurality of spectral bands corresponding to the plurality of satellite images, automatically processing the plurality of spectral bands to convert digital number of each pixel of the plurality of spectral bands into reflectance or radiance values, and creating the training set in the form of creating pixel-wise shapefiles corresponding to each of the plurality of LULC classes.--, in [0006]-[0009]; and, -- the computer-implemented method may further comprise receiving a second input defining a second time frame, automatically retrieving a second set of satellite images corresponding to the geographic area and the second time frame, automatically classifying the second set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting a comparison of the land use land cover (LULC) classes of the first and the second set of satellite images, the comparison illustrating a quantitative relative change in the land use land cover (LULC) classes of the geographic area over a time duration from the first time frame to the second time frame.
[0017] According to another exemplary embodiment, a system for generating land use land cover (LULC) classification of a geographic area is described. The system comprises at least one processor and at least one computer readable memory coupled to the at least one processor, and the processor is configured to perform all or some steps of the method described above.
[0018] According to another exemplary embodiment, a non-transitory computer readable medium is described. The non-transitory computer readable medium comprises a computer-readable code comprising instructions, which when executed by a processor, causes the processor to perform all or some steps of the method described above.
[0019] It is an object of the invention to provide a Geo-spatial artificial intelligence (Geo-AI) based fully automated computer-based method and system for predicting land use land cover (LULC) classification of a geographic area using a trained deep learning model--, in [0016]-[0019]).
Re Claims 31-33, claims 31-33 are the corresponding system claim to claims 1-3 respectively. Thus, claims 31-33 are rejected for reasons similar to those discussed in regard to claims 1-3. Furthermore, Veronesi as modified by and Jha and Gibson further disclose system of training a second machine learning model to identify an expected distribution of mapping categories for a geographic area at a given time, comprising: processing circuitry configured to perform the functions (see Veronesi: e.g., --receiving a point cloud of a digital surface model of the wooded area; concatenating data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in abstract;
--the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]; also see Gibson: e.g., -- computer-implemented methods and systems are provided for pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system. To do this, the amount that each class is contained within each area image relative to the whole dataset is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken-, in abstract, and, -- computer-implemented methods and systems for generating training data for training a machine learning system to automatically extract topographic features. In particular, aspects described herein relate to a method of pre-processing image data to generate improved training data.--, in [0001], and, -- pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system, with each image containing a part of the geographic region of interest. To do this, the amount that each class is contained within each area image relative to the whole dataset (i.e., the whole geographic region captured) is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken. This ensures that the distribution of patches taken from each area prioritises features that are less common….. a computer-implemented method of processing image data for use in training a machine learning system for classifying image data, the method comprising obtaining image data comprising a plurality of images, each image corresponding to a respective geographic area, processing the image data to identify one or more classes of topographic feature contained within each image, wherein processing the image data comprises determining a quantity of each respective topographic feature class contained within each image, generating a first dataset comprising a set of values for each image, wherein each value is representative of the quantity of one of the plurality of topographic feature classes contained within the respective image, and processing the first dataset to determine a number of samples required from each image for each topographic feature class. Processing the first dataset comprises (i) generating a normalised dataset, wherein the values of the first dataset are normalised across each image and each topographic feature class, and (ii) for each topographic feature class, calculating the number of samples required from each image based on the normalised dataset and a target number of samples for the respective topographic feature class. --, in [0008]-[0010]).
Re Claim 91, Veronesi as modified by and Jha and Gibson further disclose wherein generating the vector based on the temporal distribution of mapping categories and confidences observed for the geographic area over the time period (see Veronesi: e.g., --receiving a point cloud of a digital surface model of the wooded area; concatenating data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in abstract;
--the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]; also see Gibson: e.g., -- computer-implemented methods and systems are provided for pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system. To do this, the amount that each class is contained within each area image relative to the whole dataset is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken-, in abstract, and, -- computer-implemented methods and systems for generating training data for training a machine learning system to automatically extract topographic features. In particular, aspects described herein relate to a method of pre-processing image data to generate improved training data.--, in [0001], and, -- pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system, with each image containing a part of the geographic region of interest. To do this, the amount that each class is contained within each area image relative to the whole dataset (i.e., the whole geographic region captured) is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken. This ensures that the distribution of patches taken from each area prioritises features that are less common….. a computer-implemented method of processing image data for use in training a machine learning system for classifying image data, the method comprising obtaining image data comprising a plurality of images, each image corresponding to a respective geographic area, processing the image data to identify one or more classes of topographic feature contained within each image, wherein processing the image data comprises determining a quantity of each respective topographic feature class contained within each image, generating a first dataset comprising a set of values for each image, wherein each value is representative of the quantity of one of the plurality of topographic feature classes contained within the respective image, and processing the first dataset to determine a number of samples required from each image for each topographic feature class. Processing the first dataset comprises (i) generating a normalised dataset, wherein the values of the first dataset are normalised across each image and each topographic feature class, and (ii) for each topographic feature class, calculating the number of samples required from each image based on the normalised dataset and a target number of samples for the respective topographic feature class. --, in [0008]-[0010]; alsosee Gibson: e.g., -- computer-implemented methods and systems are provided for pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system. To do this, the amount that each class is contained within each area image relative to the whole dataset is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken-, in abstract, and, --to obtain and update the vector features of the topographic data is to process aerial imagery and extract the vector information therefrom. To do this, machine learning systems can be trained to do this automatically… A deep learning model needs to be “trained” by showing it samples of imagery along with labels stating what class each pixel of the imagery belongs to.--, in [0004]-[0006]; also see: -- computer-implemented methods and systems for generating training data for training a machine learning system to automatically extract topographic features. In particular, aspects described herein relate to a method of pre-processing image data to generate improved training data.--, in [0001], and, -- pre-processing image data to generate improved training data for training a machine learning system to automatically classify imagery. One or more images of a geographic region are captured and processed to obtain a plurality of labelled samples (also referred to as patches) for training the machine learning system, with each image containing a part of the geographic region of interest. To do this, the amount that each class is contained within each area image relative to the whole dataset (i.e., the whole geographic region captured) is measured, with the distribution of classes being weighted according to this measurement and normalised to determine how many patches per class and per area should be taken. This ensures that the distribution of patches taken from each area prioritises features that are less common….. a computer-implemented method of processing image data for use in training a machine learning system for classifying image data, the method comprising obtaining image data comprising a plurality of images, each image corresponding to a respective geographic area, processing the image data to identify one or more classes of topographic feature contained within each image, wherein processing the image data comprises determining a quantity of each respective topographic feature class contained within each image, generating a first dataset comprising a set of values for each image, wherein each value is representative of the quantity of one of the plurality of topographic feature classes contained within the respective image, and processing the first dataset to determine a number of samples required from each image for each topographic feature class. Processing the first dataset comprises (i) generating a normalised dataset, wherein the values of the first dataset are normalised across each image and each topographic feature class, and (ii) for each topographic feature class, calculating the number of samples required from each image based on the normalised dataset and a target number of samples for the respective topographic feature class. --, in [0008]-[0010]; -- [0060] The result is a probability distribution which ensures for each class, a certain number of patches will be selected containing each class. That is to say, it ensures the existence of the class is present in X patches.--, in [0060]; and, -- [0069] An example of this approach is illustrated by FIG. 5, which shows three candidate patches X, Y and Z. The distribution processing has determined that two patches of vegetation class 50 and two patches of building class 52 are needed for input to the machine learning system. The patches are selected by the least prevalent class, filling the distribution until full. In this case, the vegetation class 50 makes up 80% and the building class 52 makes up 20%, and so patches Y and Z are selected first in order to provide the two patches of building class 52 required by the distribution. This leaves only patch X for the vegetation class 50, and so patch X will be selected, along with an augmented patch X′, to thereby provide the two patches of vegetation class 50 required by the distribution.--, in [0069];
and, --and a topographic extraction program 738, which implement different aspects described herein when run by the CPU 702. Specifically, the distribution calculation program 734 is configured to receive the classification data 722 and image data 724 to perform the method of optimizing the distribution of sample areas as described with reference to FIG. 3, from which the distribution data 726 is generated and stored…to perform the method of selecting a training dataset from the patches available to meet a target allocation described with reference to FIG. 5, from which allocation data 728 and thus training data 730 is generated and stored. Similarly, the topographic feature extraction program 738 is configured to receive the training data 730, from which it is trained to classify images.--, in [0099]) further comprises:
identifying a mapping category distribution for each respective portion of a plurality of portions of the time period, to obtain a plurality of mapping category distributions for the time period (see Veronesi: e.g., --the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]; further see Jha: e.g., -- a computer-implemented method for generating land use land cover (LULC) classification of a geographic area is described. The computer-implemented method comprises receiving a first input defining a geographic area and a first time frame. The computer-implemented method further comprises automatically retrieving a first set of satellite images corresponding to the geographic area and the first time frame. The computer-implemented method further comprises automatically classifying the first set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting the LULC classification of the geographic area.
[0007] According to an example, the plurality of land use land cover (LULC) classes may include at least one of vegetation cover, surface water cover, built-up area, barren/open land, and cropland.
[0008] According to an example, the computer-implemented method may further comprise creating a training set including a plurality of satellite images, and automatically training a deep learning model using the training set and a neural network to develop the trained deep learning model.
[0009] According to an example, creating a training set may further comprise automatically retrieving a plurality of satellite images corresponding to a plurality of geographic areas, automatically fetching a plurality of spectral bands corresponding to the plurality of satellite images, automatically processing the plurality of spectral bands to convert digital number of each pixel of the plurality of spectral bands into reflectance or radiance values, and creating the training set in the form of creating pixel-wise shapefiles corresponding to each of the plurality of LULC classes.--, in [0006]-[0009]; and, -- the computer-implemented method may further comprise receiving a second input defining a second time frame, automatically retrieving a second set of satellite images corresponding to the geographic area and the second time frame, automatically classifying the second set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting a comparison of the land use land cover (LULC) classes of the first and the second set of satellite images, the comparison illustrating a quantitative relative change in the land use land cover (LULC) classes of the geographic area over a time duration from the first time frame to the second time frame.
[0017] According to another exemplary embodiment, a system for generating land use land cover (LULC) classification of a geographic area is described. The system comprises at least one processor and at least one computer readable memory coupled to the at least one processor, and the processor is configured to perform all or some steps of the method described above.
[0018] According to another exemplary embodiment, a non-transitory computer readable medium is described. The non-transitory computer readable medium comprises a computer-readable code comprising instructions, which when executed by a processor, causes the processor to perform all or some steps of the method described above.
[0019] It is an object of the invention to provide a Geo-spatial artificial intelligence (Geo-AI) based fully automated computer-based method and system for predicting land use land cover (LULC) classification of a geographic area using a trained deep learning model--, in [0016]-[0019]); and
obtaining the vector based on concatenating the plurality of mapping category distributions (see Veronesi: e.g., --the data processing system may train machine learning models to predict timber data for images over time and select the models that make the most accurate predictions to use in practice. For example, after inputting a series of training data sets into the machine learning models for training, the data processing system may evaluate the accuracy of the models by comparing the models' outputs against the expected values. The data processing system may select the machine learning model with the highest accuracy to use upon receiving a request to generate timber data for a geographical area.--, in [0058]; also see: --Filling intelligence gaps in-between surveys is a challenge as is getting ground resources to the right place at the right time to maximize impact. … providing an improved forest inventory by capturing a distribution and intermixing of different tree species within a forest and estimating a total volume and biomass of available timber in forest areas. Advantageously, the improved forest inventory system models tree count, height, and parameters to characterize the forest using optical data, synthetic-aperture radar (SAR) data, topographical data, and other data. The system, method, apparatus, and computer-readable medium described herein provide a technical improvement to modeling forests.
[0005] In accordance with some embodiments of the present disclosure, a method is disclosed. The method may include receiving, by one or more processors, an image depicting an overhead view of a wooded area, the image comprising a plurality of pixels; receiving, by the one or more processors, a set of climate data for a geographic region in which the wooded area is located; receiving, by the one or more processors, a point cloud of a digital surface model of the wooded area; concatenating, by the one or more processors, data corresponding to the plurality of pixels of the image, the set of climate data, and the point cloud into a feature vector; executing, by the one or more processors, a machine learning model using the feature vector to generate timber data for each of the plurality of pixels of the image; and generating, by the one or more processors, an interactive overlay from the timber data, the interactive overlay comprising the generated timber data for each of the plurality of pixels of the image.--, in [0003]-[0005]; and, --getting ground resources to the right place at the right time to maximize impact. --, in [0021]; --the use of satellite imagery and artificial intelligence (AI) processing techniques to remotely provide a view of an entire forest inventory across vast geographic areas and to analyze disturbance events that threaten its value. This solution helps manage inventory, carbon stock, fire damage, pest, and disease, brushing, and mill optimization.
[0025] Advantageously, the embodiments described herein track the full forest lifecycle across seasons, fusing satellite and multiple data feeds with advanced AI….Digital surface model data can be included in the list of predictors to further increase the accuracy of the model output. These sources are used to generate inputs to a model. The inputs can be SAR indices, spectral indices, and values for topographic variables.
[0028] The model may generate species distribution (e.g., the distribution and intermixing of different tree species within a forest) and/or tree mensuration (e.g., estimates of the total volume and/or biomass of available timber in forest areas and additionally models of total tree count, height and/or the diameter at breast height (DBH) parameters) data.--, in [0025]-[0028]; further see Jha: e.g., -- a computer-implemented method for generating land use land cover (LULC) classification of a geographic area is described. The computer-implemented method comprises receiving a first input defining a geographic area and a first time frame. The computer-implemented method further comprises automatically retrieving a first set of satellite images corresponding to the geographic area and the first time frame. The computer-implemented method further comprises automatically classifying the first set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting the LULC classification of the geographic area.
[0007] According to an example, the plurality of land use land cover (LULC) classes may include at least one of vegetation cover, surface water cover, built-up area, barren/open land, and cropland.
[0008] According to an example, the computer-implemented method may further comprise creating a training set including a plurality of satellite images, and automatically training a deep learning model using the training set and a neural network to develop the trained deep learning model.
[0009] According to an example, creating a training set may further comprise automatically retrieving a plurality of satellite images corresponding to a plurality of geographic areas, automatically fetching a plurality of spectral bands corresponding to the plurality of satellite images, automatically processing the plurality of spectral bands to convert digital number of each pixel of the plurality of spectral bands into reflectance or radiance values, and creating the training set in the form of creating pixel-wise shapefiles corresponding to each of the plurality of LULC classes.--, in [0006]-[0009]; and, -- the computer-implemented method may further comprise receiving a second input defining a second time frame, automatically retrieving a second set of satellite images corresponding to the geographic area and the second time frame, automatically classifying the second set of satellite images into a plurality of land use land cover (LULC) classes using a trained deep learning model, and automatically presenting a visualization depicting a comparison of the land use land cover (LULC) classes of the first and the second set of satellite images, the comparison illustrating a quantitative relative change in the land use land cover (LULC) classes of the geographic area over a time duration from the first time frame to the second time frame.
[0017] According to another exemplary embodiment, a system for generating land use land cover (LULC) classification of a geographic area is described. The system comprises at least one processor and at least one computer readable memory coupled to the at least one processor, and the processor is configured to perform all or some steps of the method described above.
[0018] According to another exemplary embodiment, a non-transitory computer readable medium is described. The non-transitory computer readable medium comprises a computer-readable code comprising instructions, which when executed by a processor, causes the processor to perform all or some steps of the method described above.
[0019] It is an object of the invention to provide a Geo-spatial artificial intelligence (Geo-AI) based fully automated computer-based method and system for predicting land use land cover (LULC) classification of a geographic area using a trained deep learning model--, in [0016]-[0019]).
Conclusion
Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WEI WEN YANG whose telephone number is (571)270-5670. The examiner can normally be reached on 8:00 - 5:00 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached on 571-272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WEI WEN YANG/Primary Examiner, Art Unit 2662
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