In variants, the method can include: determining a timeseries of measurements of a geographic region; determining a set of object representations from the timeseries of measurements; and determining a timeseries of object versions based on relationships between the object representations.
In variants, a method for determining properties of an object within a target image captured by remote sensors can include: training a model to determine geometric object properties for a set of geometric objects depicted in a training image data set, and applying the model to the target image to determine a target geometric object property for a target geometric object within the target image.
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
In variants, the method for subjective property scoring can include determining an objective score for a subjective characteristic of a property using a model trained using subjective labels for a set of training properties. In examples, the model can be trained on subjective property rankings, determined using the subjective labels, for the set of training properties.
In variants, the method for change analysis can include: training a representation model and evaluating a geographic region. In an example, the method for change analysis can include detecting a rare change in a geographic region by comparing a first and second representation, extracted from a first and second geographic region measurement sampled at a first and second time, respectively, using a common-change-agnostic model.
Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.
G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
H04N 23/10 - Cameras or camera modules comprising electronic image sensorsControl thereof for generating image signals from different wavelengths
H04N 23/11 - Cameras or camera modules comprising electronic image sensorsControl thereof for generating image signals from different wavelengths for generating image signals from visible and infrared light wavelengths
In variants, the method for automatic debris detection includes: determining a region image; optionally determining a parcel representation for the region image; generating a debris representation using the region image; generating a debris score based on the debris representation; and optionally monitoring the debris score over time.
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a patternLocating or processing of specific regions to guide the detection or recognition
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
In variants, a method for property group analysis can include: determining a property, determining whether the property is part of a group, identifying other properties within the group, optionally determining whether to merge groups, and optionally providing a final group. However, the method can additionally and/or alternatively include any other suitable elements.
In variants, the method for change analysis can include detecting a rare change in a geographic region by comparing a first representation and a second representation, extracted from a first geographic region measurement and a second geographic region measurement sampled at a first time and a second time, respectively, using a common-change-agnostic model.
G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 20/17 - Terrestrial scenes taken from planes or by drones
9.
SYSTEMS, METHODS, AND COMPUTER READABLE MEDIA FOR PREDICTIVE ANALYTICS AND CHANGE DETECTION FROM REMOTELY SENSED IMAGERY
Systems and methods are provided for automatically detecting a change in a feature. For example, a system includes a memory and a processor configured to analyze a change associated with a feature over a period of time using a plurality of remotely sensed time series images. Upon execution, the system would receive a plurality of remotely sensed time series images, extract a feature from the plurality of remotely sensed time series images, generate at least two time series feature vectors based on the feature, where the at least two time series feature vectors correspond to the feature at two different times, create a neural network model configured to predict a change in the feature at a specified time, and determine, using the neural network model, the change in the feature at a specified time based on a change between the at least two time series feature vectors.
G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
In variants, the method for change analysis can include: training a representation model and evaluating a geographic region. In an example, the method for change analysis can include detecting a rare change in a geographic region by comparing a first and second representation, extracted from a first and second geographic region measurement sampled at a first and second time, respectively, using a common-change-agnostic model.
G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 20/17 - Terrestrial scenes taken from planes or by drones
11.
SYSTEMS, METHODS, AND COMPUTER READABLE MEDIA FOR PREDICTIVE ANALYTICS AND CHANGE DETECTION FROM REMOTELY SENSED IMAGERY
Systems and methods are provided for automatically detecting a change in a feature. For example, a system includes a memory and a processor configured to analyze a change associated with a feature over a period of time using a plurality of remotely sensed time series images. Upon execution, the system would receive a plurality of remotely sensed time series images, extract a feature from the plurality of remotely sensed time series images, generate at least two time series feature vectors based on the feature, where the at least two time series feature vectors correspond to the feature at two different times, create a neural network model configured to predict a change in the feature at a specified time, and determine, using the neural network model, the change in the feature at a specified time based on a change between the at least two time series feature vectors.
G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
In variants, the method for 3D modeling can include: determining a property of interest, determining property information for the property of interest, determining property component parameter values based on property information, determining a 3D model based on property component parameter values, and optionally determining a set of property attributes. However, the method can additionally and/or alternatively include any other suitable elements.
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
In variants, the method for 3D modeling can include: determining a property of interest, determining property information for the property of interest, determining property component parameter values based on property information, determining a 3D model based on property component parameter values, and optionally determining a set of property attributes. However, the method can additionally and/or alternatively include any other suitable elements.
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.
G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
H04N 23/10 - Cameras or camera modules comprising electronic image sensorsControl thereof for generating image signals from different wavelengths
H04N 23/11 - Cameras or camera modules comprising electronic image sensorsControl thereof for generating image signals from different wavelengths for generating image signals from visible and infrared light wavelengths
Systems and methods for property condition analysis comprising: determining a measurement, optionally determining a set of property attributes, determining a condition score, optionally providing the condition score, and optionally training a condition scoring model, determining a measurement depicting a property; determining parcel data associated with the property; determining a set of attributes for the property, based on the measurement and the parcel data, using a set of attribute models; and determining a condition score based on the set of attributes using a condition scoring model.
In variants, the method for subjective property scoring can include determining an objective score for a subjective characteristic of a property using a model trained using subjective labels for a set of training properties. In examples, the model can be trained on subjective property rankings, determined using the subjective labels, for the set of training properties.
In variants, the method for property condition analysis can include: determining a measurement, optionally determining a set of property attributes, determining a condition score, optionally providing the condition score, and optionally training a condition scoring model.
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
In variants, the method for property condition analysis can include: determining a measurement, optionally determining a set of property attributes, determining a condition score, optionally providing the condition score, and optionally training a condition scoring model.
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
A method for environmental evaluation of a property (e.g., determining a hazard score for a property) can include: determining a property; determining measurements for the property; determining attribute values for the property; determining an evaluation metric (e.g., hazard score) for the property; and optionally training one or more environmental evaluation models.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
G06V 10/94 - Hardware or software architectures specially adapted for image or video understanding
In variants, the method can include: determining a timeseries of measurements of a geographic region; determining a set of object representations from the timeseries of measurements; and determining a timeseries of object versions based on relationships between the object representations.
The method for property typicality determination can include: determining a property, determining attribute values for the property, determining a reference population for the property, determining reference population attribute values, determining a typicality metric for the property, and optionally determining an influential attribute.
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
In variants, a method for property group analysis can include: determining a property, determining whether the property is part of a group, identifying other properties within the group, optionally determining whether to merge groups, and optionally providing a final group. However, the method can additionally and/or alternatively include any other suitable elements.
In variants, a method for property analysis can include: determining a property of interest, determining property information for the property, determining property attributes for the property, determining a value for the property, and optionally adjusting the value for the property. However, the method can additionally and/or alternatively include any other suitable elements.
In variants, the method can include: determining a timeseries of measurements of a geographic region; determining a set of object representations from the timeseries of measurements; and determining a timeseries of object versions based on relationships between the object representations.
In variants, the method for change analysis can include detecting a rare change in a geographic region by comparing a first and second representation, extracted from a first and second geographic region measurement sampled at a first and second time, respectively, using a common-change-agnostic model.
G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 20/17 - Terrestrial scenes taken from planes or by drones
In variants, the method can include: determining a timeseries of measurements of a geographic region; determining a set of object representations from the timeseries of measurements; and determining a timeseries of object versions based on relationships between the object representations. The timeseries of structure versions can be determined by: extracting a structure representation for a physical structure (e.g., a roof vector, a roof segment, a roof geometry, a building vector, a building segment, etc.) from each measurement of the timeseries; and associating structure representations representing the same structure version with a common structure version (e.g., the same structure identifier). Structure representations representing the same structure version can have the same representation values, have the same structural segments (e.g., physical segments, image segments, geometric segments, etc.), share structural segments, and/ or otherwise represent the same structure version.
G06F 18/211 - Selection of the most significant subset of features
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
G06T 7/62 - Analysis of geometric attributes of area, perimeter, diameter or volume
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
G06V 10/771 - Feature selection, e.g. selecting representative features from a multi-dimensional feature space
G06F 18/2132 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods based on discrimination criteria, e.g. discriminant analysis
G06T 7/33 - Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
In variants, the method for subjective property scoring can include determining an objective score for a subjective characteristic of a property using a model trained using subjective property rankings.
In variants, the method can include: determining a timeseries of measurements of a geographic region; determining a set of object representations from the timeseries of measurements; and determining a timeseries of object versions based on relationships between the object representations.
In variants, the method can include: detecting a property within a set of measurements; determining a set of property parameters based on the detection; determining a set of higher-resolution measurements based on the set of property parameters; and determining a set of property attributes based on the set of higher-resolution measurements.
In variants, the method for occlusion correction can include: determining a measurement depicting an occluded object of interest (OOI), optionally infilling the occluded portion of the object of interest within the measurement, and determining an attribute of the object of interest based on the infilled measurement.
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
In variants, the method for change analysis can include detecting a rare change in a geographic region by comparing a first and second representation, extracted from a first and second geographic region measurement sampled at a first and second time, respectively, using a common-change-agnostic model.
In variants, the method for change analysis can include detecting a rare change in a geographic region by comparing a first and second representation, extracted from a first and second geographic region measurement sampled at a first and second time, respectively, using a common-change-agnostic model.
G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
G06V 20/17 - Terrestrial scenes taken from planes or by drones
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
33.
Systems and methods for analyzing remote sensing imagery
Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.
G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
H04N 23/11 - Cameras or camera modules comprising electronic image sensorsControl thereof for generating image signals from different wavelengths for generating image signals from visible and infrared light wavelengths
In variants, a method for viewshed analysis can include: determining a location, determining a set of location viewpoints for the location, determining a viewshed for the location, determining a set of view factors for the location, and determining a view factor representation for the location based the viewshed and the set of view factors, optionally determining a view parameter for the location, and/or any other suitable elements.
The method for property typicality determination can include: determining a property, determining attribute values for the property, determining a reference population for the property, determining reference population attribute values, determining a typicality metric for the property, and optionally determining an influential attribute.
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
In variants, the method for occlusion correction can include: determining a measurement depicting an occluded object of interest (001 ), optionally infilling the occluded portion of the object of interest within the measurement, and determining an attribute of the object of interest based on the infilled measurement, and optionally determining a property analysis based on the attribute values.
In variants, the method for occlusion correction can include: determining a measurement depicting an occluded object of interest (OOI), optionally infilling the occluded portion of the object of interest within the measurement, and determining an attribute of the object of interest based on the infilled measurement.
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
In variants, the method for property condition analysis can include: determining a measurement, optionally determining a set of property attributes, determining a condition score, optionally providing the condition score, and optionally training a condition scoring model.
Systems and methods for property condition analysis comprising: determining a measurement, optionally determining a set of property attributes, determining a condition score, optionally providing the condition score, and optionally training a condition scoring model, determining a measurement depicting a property; determining parcel data associated with the property; determining a set of attributes for the property, based on the measurement and the parcel data, using a set of attribute models; and determining a condition score based on the set of attributes using a condition scoring model.
Determining a hazard score of a property can include: determining a property; determining measurements for the property; determining attribute values for the property; determining a hazard score for the property; and optionally training one or more hazard models.
In variants, the method for automatic debris detection includes: determining a region image; optionally determining a parcel representation for the region image; generating a debris representation using the region image; generating a debris score based on the debris representation; and optionally monitoring the debris score over time.
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a patternLocating or processing of specific regions to guide the detection or recognition
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
In variants, the method for automatic debris detection includes: determining a region image; optionally determining a parcel representation for the region image; generating a debris representation using the region image; generating a debris score based on the debris representation; and optionally monitoring the debris score over time.
G06K 9/46 - Extraction of features or characteristics of the image
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a patternLocating or processing of specific regions to guide the detection or recognition
G06K 9/62 - Methods or arrangements for recognition using electronic means
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
In variants, the method for automatic debris detection includes: determining a region image; optionally determining a parcel representation for the region image; generating a debris representation using the region image; generating a debris score based on the debris representation; and optionally monitoring the debris score over time.
36 - Financial, insurance and real estate services
Goods & Services
(1) Providing insurance information; Providing insurance information used to pre-populate insurance documents and forms with relevant data; Providing insurance information including aggregated data from multiple data sources including data derived through the use of artificial intelligence (AI) to generate pre-populated information for insurance documents and forms; Providing aggregated and AI generated insurance information for use by insurance providers in relation to underwriting, pricing, and claims evaluation; Providing insurance information including substantiated data by evaluating multiple and related sources and through the application of intelligent models to raw data.
The method for determining a geographic identifier including: determining a location description; determining parcel data; determining a georeferenced image based on the location description; generating a set of image features using the georeferenced image; optionally determining a built structure class; identifying a location of interest within the georeferenced image based on the set of features; determining a geographic identifier associated with the location of interest based on the image georeference; associating the geographic identifier with the location description; optionally returning the geographic identifier in response to the location description comprising an address; and optionally returning an address in response to the location description comprising a geographic coordinates.
G06F 16/587 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
Systems and methods are provided for automatically detecting a change in a feature. For example, a system includes a memory and a processor configured to analyze a change associated with a feature over a period of time using a plurality of remotely sensed time series images. Upon execution, the system would receive a plurality of remotely sensed time series images, extract a feature from the plurality of remotely sensed time series images, generate at least two time series feature vectors based on the feature, where the at least two time series feature vectors correspond to the feature at two different times, create a neural network model configured to predict a change in the feature at a specified time, and determine, using the neural network model, the change in the feature at a specified time based on a change between the at least two time series feature vectors.
G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
The method for determining property feature segmentation includes: receiving a region image for a region; determining parcel data for the region; determining a final segmentation output based on the region image and parcel data using a trained segmentation module; optionally generating training data; and training a segmentation module using the training data S500.
Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.
G06K 9/62 - Methods or arrangements for recognition using electronic means
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
ABSTRACT The method for determining property feature segmentation includes: receiving a region image for a region; determining parcel data for the region; determining a final segmentation output based on the region image and parcel data using a trained segmentation module; optionally generating training data; and training a segmentation module using the training data. 8308399 Date recue/Date received 2023-03-29
The method for determining property feature segmentation includes: receiving a region image for a region; determining parcel data for the region; determining a final segmentation output based on the region image and parcel data using a trained segmentation module; optionally generating training data; and training a segmentation module using the training data S500.
The method for determining property feature segmentation includes: receiving a region image for a region; determining parcel data for the region; determining a final segmentation output based on the region image and parcel data using a trained segmentation module; optionally generating training data; and training a segmentation module using the training data S500.
The method for determining a geographic identifier including: determining a location description; determining parcel data; determining a georeferenced image based on the location description; generating a set of image features using the georeferenced image; optionally determining a built structure class; identifying a location of interest within the georeferenced image based on the set of features; determining a geographic identifier associated with the location of interest based on the image georeference; associating the geographic identifier with the location description; optionally returning the geographic identifier in response to the location description comprising an address; and optionally returning an address in response to the location description comprising a geographic coordinates.
The method for determining a geographic identifier including: determining a location description; determining parcel data; determining a georeferenced image based on the location description; generating a set of image features using the georeferenced image; optionally determining a built structure class; identifying a location of interest within the georeferenced image based on the set of features; determining a geographic identifier associated with the location of interest based on the image georeference; associating the geographic identifier with the location description; optionally returning the geographic identifier in response to the location description comprising an address; and optionally returning an address in response to the location description comprising a geographic coordinates.
G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
G06F 16/587 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Providing access to information in the fields of real estate and insurance, namely, attributes of property, property condition, and surrounding area; Providing access to information in the fields of real estate and insurance, namely, attributes of property, property condition, and surrounding area for use in insurance, risk management, risk assessment, risk analysis, property management, investment, valuation and lending.
(2) Providing temporary use of non-downloadable computer software for accessing information in the fields of real estate and insurance, namely, attributes of property, property condition, and surrounding area; Providing temporary use of non-downloadable computer software for accessing information in the fields of real estate and insurance, namely, attributes of property, property condition, and surrounding area for use in insurance, risk management, risk assessment, risk analysis, property management, investment, valuation and lending.
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Providing access to information in the fields of real estate and insurance, namely, attributes of property, property condition, and surrounding area; Providing access to information in the fields of real estate and insurance, namely, attributes of property, property condition, and surrounding area for use in insurance, risk management, risk assessment, risk analysis, property management, investment, valuation and lending.
(2) Providing temporary use of non-downloadable computer software for accessing information in the fields of real estate and insurance, namely, attributes of property, property condition, and surrounding area; Providing temporary use of non-downloadable computer software for accessing information in the fields of real estate and insurance, namely, attributes of property, property condition, and surrounding area for use in insurance, risk management, risk assessment, risk analysis, property management, investment, valuation and lending.
42 - Scientific, technological and industrial services, research and design
Goods & Services
Providing multiple user access to proprietary collections of information by means of global computer information networks related to property, property condition, and surrounding area; Providing multiple user access to proprietary collections of information by means of global computer information networks regarding property, property condition, and surrounding area for use in insurance, risk management, risk assessment, risk analysis, property management, investment, valuation, and lending; Providing multiple user access to proprietary collections of information by means of global computer information networks to information related to property, property condition, and surrounding area aggregated from remotely sensed imagery of property, public records, and additional data sources Providing temporary use of non-downloadable computer software for accessing information related to property, property condition, and surrounding area; Providing temporary use of non-downloadable computer software for accessing information regarding property, property condition, and surrounding area for use in insurance, risk management, risk assessment, risk analysis, property management, investment, valuation and lending; Providing temporary use of non-downloadable computer software for accessing information related to property, property condition, and surrounding area aggregated from remotely sensed imagery of property, public records, and additional data sources
42 - Scientific, technological and industrial services, research and design
Goods & Services
Providing multiple user access to proprietary collections of information by means of global computer information networks related to property, property condition, and surrounding area; Providing multiple user access to proprietary collections of information by means of global computer information networks regarding property, property condition, and surrounding area for use in insurance, risk management, risk assessment, risk analysis, property management, investment, valuation and lending; Providing multiple user access to proprietary collections of information by means of global computer information networks related to property, property condition, and surrounding area aggregated from remotely sensed imagery of property, public records, and additional data sources Providing temporary use of non-downloadable computer software for accessing information related to property, property condition, and surrounding area; Providing temporary use of non-downloadable computer software for accessing information regarding property, property condition, and surrounding area for use in insurance, risk management, risk assessment, risk analysis, property management, investment, valuation and lending; Providing temporary use of non-downloadable computer software for accessing information related to property, property condition, and surrounding area aggregated from remotely sensed imagery of property, public records, and additional data sources
58.
Systems and methods for analyzing remote sensing imagery
Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.
Systems and methods are provided for automatically detecting a change in a feature. For example, a system includes a memory and a processor configured to analyze a change associated with a feature over a period of time using a plurality of remotely sensed time series images. Upon execution, the system would receive a plurality of remotely sensed time series images, extract a feature from the plurality of remotely sensed time series images, generate at least two time series feature vectors based on the feature, where the at least two time series feature vectors correspond to the feature at two different times, create a neural network model configured to predict a change in the feature at a specified time, and determine, using the neural network model, the change in the feature at a specified time based on a change between the at least two time series feature vectors.
Systems and methods are provided for automatically detecting a change in a feature. For example, a system includes a memory and a processor configured to analyze a change associated with a feature over a period of time using a plurality of remotely sensed time series images. Upon execution, the system would receive a plurality of remotely sensed time series images, extract a feature from the plurality of remotely sensed time series images, generate at least two time series feature vectors based on the feature, where the at least two time series feature vectors correspond to the feature at two different times, create a neural network model configured to predict a change in the feature at a specified time, and determine, using the neural network model, the change in the feature at a specified time based on a change between the at least two time series feature vectors.
Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.
Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.
Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.
In variants, a method for determining properties of an object within a target image captured by remote sensors can include: training a model to determine geometric object properties for a set of geometric objects depicted in a training image data set, and applying the model to the target image to determine a target geometric object property for a target geometric object within the target image.
Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.
G06K 9/62 - Methods or arrangements for recognition using electronic means
G06K 9/66 - Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix references adjustable by an adaptive method, e.g. learning
