Methods and systems for generating artificial parcel data are provided. An example method involves accessing geospatial imagery covering a geographic area of interest, subdividing the geospatial imagery into a grid of image tiles, accessing building footprint data georeferenced to the geospatial imagery representing positions and shapes of one or more buildings contained in the geographic area of interest, accessing ground truth parcel data representing boundaries of known legal land parcels defined within the geographic area of interest, cross-referencing the ground truth parcel data with the building footprint data to identify buildings that are located in areas that are not labeled by ground truth parcel data, selecting, based on the cross-referencing, one or more image tiles that contain the identified buildings, and providing the selected image tiles to an artificial parcel generator to generate artificial parcel data representing estimated boundaries of legal land parcels occupied by the identified buildings.
Example systems and methods for data augmentation for occlusion handling in feature extraction are provided. An example method involves accessing a geospatial image depicting a built landcover feature and a natural occluding feature that partially occludes the built landcover feature, extracting an occlusion map from the geospatial image, wherein the occlusion map labels areas of the geospatial image as areas either covered by the natural occluding feature or not covered by the natural occluding feature, applying the occlusion map as a mask to the geospatial image to generate a masked geospatial image, and applying a machine learning model to the masked geospatial image to extract a geometric representation of the built landcover feature as depicted in the geospatial image, wherein the machine learning model is trained using training imagery that is augmented with simulated occluding features.
Methods and systems for generating geometric models of buildings with pitched roof geometry from multiview imagery are provided. An example method involves establishing a three-dimensional coordinate space for the multiview imagery, and displaying, through an annotation platform, first and second images of the multiview imagery depicting the pitched roof from different points of view, providing functionality for a user to annotate the first and second images with at least a portion of a three-dimensional structure of the pitched roof including at least one roof facet, and reconstructing a geometric model. The method further involves providing functionality for a user to further annotate a linear roof element by constraining a starting point or ending point of the linear roof element to intersect the portion of the geometric model that represents the at least one roof facet.
G06T 19/00 - Manipulating 3D models or images for computer graphics
G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
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
Methods and systems for generating three-dimensional vector maps representing three-dimensional features are provided. An example method involves accessing multiview imagery that depicts a three-dimensional feature, applying a machine learning model to the multiview imagery to generate a representation of the three-dimensional feature, and outputting the representation.
Methods and systems for generating three-dimensional vector maps representing three-dimensional features are provided. An example method involves accessing multiview imagery that depicts a three-dimensional feature, applying a machine learning model to the multiview imagery to generate a representation of the three-dimensional feature, and outputting the representation.
Example systems and methods for configuring machine learning models to generate geometric models are provided. An example method involves obtaining geometric modelling data comprising sequences of geometric modelling operations, and training the machine learning model on the geometric modelling data to generate geometric models encoded as tokenized representations of sequences of geometric modelling operations to be performed to build the geometric models, wherein the machine learning model is trained to generate the geometric models in accordance with learned geometric modelling practices extracted from the geometric modelling data.
G06V 10/766 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using regression, e.g. by projecting features on hyperplanes
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 10/77 - 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 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
7.
Annotation tools for reconstructing three-dimensional roof geometry
Methods and systems for generating geometric models of pitched roofs of buildings based on multiview imagery are provided. An example method involves establishing a three-dimensional coordinate space for the multiview imagery, displaying, through an annotation platform, multiview imagery depicting the pitched roof from different points of view, and providing, through the annotation platform, functionality for a user to define a perimeter of the pitched roof, define a ridge line of the pitched roof, and define at least part of a roof facet of the pitched roof by connecting the ridge line of the pitched roof to the perimeter of the pitched roof.
G06F 7/48 - Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using non-contact-making devices, e.g. tube, solid state deviceMethods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using unspecified devices
G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
8.
Platform for distributed landcover feature data review
Methods and systems for extracting and reviewing landcover feature data are provided. An example method involves accessing geospatial imagery covering a geographic area and landcover feature data extracted from the geospatial imagery, subdividing the geospatial imagery into a grid of image tiles, assigning landcover feature data review technicians to review the image tiles and associated landcover feature data through a landcover feature data review platform, and receiving revised landcover feature data that modifies the landcover feature data.
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
Methods and systems for generating geometric models of buildings with from multiview imagery are provided. An example method involves establishing a three-dimensional coordinate space for the multiview imagery, displaying, through an annotation platform, first and second images of the multiview imagery depicting the building from different points of view, providing, through the annotation platform, functionality for a user to provide user input comprising instructions that annotate the first and second images with at least two adjacent building sections of the building having roofs at different heights, and reconstructing, based on the user input received through the annotation platform, a geometric model comprising at least the two adjacent building sections.
Methods and systems for enhancing elevation models with landcover feature data are provided. An example method involves accessing geospatial imagery covering an area of interest that includes a three-dimensional landcover feature, generating a digital surface model that includes one or more above-ground points corresponding to the three-dimensional landcover feature, extracting two-dimensional vector data from the geospatial imagery that represents a two-dimensional boundary of the three-dimensional landcover feature, generating a digital terrain model by removing from the digital surface model a portion that is bound by the two-dimensional vector data, generating three-dimensional vector data representing the three-dimensional landcover feature, inserting one or more ground-level points of the three-dimensional vector data into the digital terrain model, and filling the digital terrain model where the portion of the digital surface model was removed with estimated ground-level points of the three-dimensional vector data as a seed in a non-linear optimization process.
Methods and systems for determining the height of structures based on imagery of the structures and associated two-dimensional vector data are provided. An example method involves projecting two-dimensional vector data outlining a roof of a structure into images of the structure captured from different perspectives and feature matching the vector data across the imagery to determine a best-matching three-dimensional position for the roof situated at the height of the structure.
G06V 10/40 - Extraction of image or video features
G06V 10/46 - Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]Salient regional features
G06V 10/98 - Detection or correction of errors, e.g. by rescanning the pattern or by human interventionEvaluation of the quality of the acquired patterns
Methods and systems for extracting and reviewing landcover feature data are provided. An example method involves accessing geospatial imagery covering a geographic area and landcover feature data extracted from the geospatial imagery, subdividing the geospatial imagery into a grid of image tiles, assigning landcover feature data review technicians to review the image tiles and associated landcover feature data through a landcover feature data review platform, and receiving revised landcover feature data that modifies the landcover feature data.
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
Methods and systems for generating geometric models of buildings with from multiview imagery are provided. An example method involves establishing a three-dimensional coordinate space for the multiview imagery, displaying, through an annotation platform, first and second images of the multiview imagery depicting the building from different points of view, providing, through the annotation platform, functionality for a user to provide user input comprising instructions that annotate the first and second images with at least two adjacent building sections of the building having roofs at different heights, and reconstructing, based on the user input received through the annotation platform, a geometric model comprising at least the two adjacent building sections.
Methods and systems for generating geometric models of buildings from multiview imagery are provided. An example method involves displaying at least a first image and a second image that depict the building from different points of view, providing functionality for a user to annotate, with reference to at least the first image and the second image, a three-dimensional structure of a first building section of the building having a polygonal outline of a first substantially horizontal roof structure situated at a first height, and a three-dimensional structure of a second building section of the building having a polygonal outline of a second substantially horizontal roof structure situated at a second height lower than the first height. The polygonal outline of the second substantially horizontal roof structure is annotated by selecting a point located directly on one of the side walls of the first building section.
Methods and systems for extracting and reviewing landcover feature data are provided. An example method involves accessing geospatial imagery covering a geographic area and landcover feature data extracted from the geospatial imagery, subdividing the geospatial imagery into a grid of image tiles, assigning landcover feature data review technicians to review the image tiles and associated landcover feature data through a landcover feature data review platform, and receiving revised landcover feature data that modifies the landcover feature data.
G06V 10/40 - Extraction of image or video features
G06V 10/46 - Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]Salient regional features
G06V 10/98 - Detection or correction of errors, e.g. by rescanning the pattern or by human interventionEvaluation of the quality of the acquired patterns
18.
ENHANCING ELEVATION MODELS WITH LANDCOVER FEATURE DATA
Methods and systems for enhancing elevation models with landcover feature data are provided. An example method involves accessing imagery covering an area of interest that contains an above-ground feature, generating, a digital surface model of the area of interest, extracting three-dimensional landcover feature data representing the above-ground feature that contains one or more actual ground-level points at the base of the above-ground feature, and generating a digital terrain model from the digital surface model at least in part by removing the above-ground feature from the digital surface model and filling at least some of the removed area with one or more of the actual ground-level points from the three-dimensional landcover feature data.
Methods and systems for enhancing elevation models with landcover feature data are provided. An example method involves accessing imagery covering an area of interest that contains an above-ground feature, generating, a digital surface model of the area of interest, extracting three-dimensional landcover feature data representing the above-ground feature that contains one or more actual ground-level points at the base of the above-ground feature, and generating a digital terrain model from the digital surface model at least in part by removing the above-ground feature from the digital surface model and filling at least some of the removed area with one or more of the actual ground-level points from the three-dimensional landcover feature data.
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
G06T 19/20 - Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
22.
Annotation tools for reconstructing three-dimensional roof geometry
Methods and systems for generating geometric models of buildings with complex roof geometry from multiview imagery are provided. An example method involves establishing a three-dimensional coordinate space for the multiview imagery, and displaying, through an annotation platform, first and second images of the multiview imagery depicting the pitched roof from different points of view. The method further involves receiving, through the annotation platform, user input annotating the first and second images with a plurality of roof elements defining a three-dimensional structure of the pitched roof, and reconstructing, based on the user input received through the annotation platform, a geometric model representing the three-dimensional structure of the pitched roof situated in the three-dimensional coordinate space.
G06T 19/00 - Manipulating 3D models or images for computer graphics
G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
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
Methods and systems for generating geometric models of buildings with complex roof geometry from multiview imagery are provided. An example method involves establishing a three-dimensional coordinate space for the multiview imagery, and displaying, through an annotation platform, first and second images of the multiview imagery depicting the pitched roof from different points of view. The method further involves receiving, through the annotation platform, user input annotating the first and second images with a plurality of roof elements defining a three-dimensional structure of the pitched roof, and reconstructing, based on the user input received through the annotation platform, a geometric model representing the three-dimensional structure of the pitched roof situated in the three-dimensional coordinate space.
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
24.
VECTOR DATA PROJECTION AND FEATURE MATCHING TO DETERMINE THREE-DIMENSIONAL STRUCTURE
G06V 10/50 - Extraction of image or video features by performing operations within image blocksExtraction of image or video features by using histograms, e.g. histogram of oriented gradients [HoG]Extraction of image or video features by summing image-intensity valuesProjection analysis
G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
25.
MACHINE LEARNING FOR ARTIFICIAL PARCEL DATA GENERATION
G06V 10/46 - Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]Salient regional features
G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
27.
MACHINE LEARNING FOR ARTIFICIAL PARCEL DATA GENERATION
Methods and systems for generating artificial parcel data are provided. An example method involves accessing geospatial imagery depicting one or more buildings and surrounding areas, applying a machine learning model to the geospatial imagery to generate artificial parcel data in a form of a distance-transform raster map that represents a legal land parcel for each of the one or more buildings, and converting the distance-transform raster map into a vector map containing one or more polygons that represent the boundaries of each of the legal land parcels.
Methods and systems for determining the height of structures based on imagery of the structures and associated two-dimensional vector data are provided. An example method involves projecting two-dimensional vector data outlining a roof of a structure into images of the structure captured from different perspectives and feature matching the vector data across the imagery to determine a best- matching three-dimensional position for the roof situated at the height of the structure.
G06V 10/50 - Extraction of image or video features by performing operations within image blocksExtraction of image or video features by using histograms, e.g. histogram of oriented gradients [HoG]Extraction of image or video features by summing image-intensity valuesProjection analysis
G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
29.
DATA AUGMENTATION FOR OCCLUSION HANDLING IN FEATURE EXTRACTION
Example systems and methods for data augmentation for occlusion handling in feature extraction are provided. An example method involves accessing a geospatial image depicting an occludable landcover feature; augmenting the geospatial image with a simulated occluding landcover feature to generate an occluding feature-augmented geospatial image, wherein the simulated occluding landcover feature partially occludes the occludable landcover feature as depicted in the geospatial image; and training a machine learning model, using the occluding feature-augmented geospatial image, to extract geometric representations of occludable landcover features as depicted in geospatial imagery.
G06V 10/46 - Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]Salient regional features
G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
30.
Vector data projection and feature matching to determine three-dimensional structure
Methods and systems for determining the height of structures based on imagery of the structures and associated two-dimensional vector data are provided. An example method involves projecting two-dimensional vector data outlining a roof of a structure into images of the structure captured from different perspectives and feature matching the vector data across the imagery to determine a best-matching three-dimensional position for the roof situated at the height of the structure.
Example systems and methods for data augmentation for occlusion handling in feature extraction are provided. An example method involves accessing a tree cover source image that depicts one or more areas of tree cover, extracting a tree cover map from the tree cover source image that labels areas of the tree cover source image as either areas covered by tree cover or not covered by tree cover, selecting a contiguous area of tree cover from the tree cover map to use as simulated tree cover, accessing a geospatial image depicting a building, placing the simulated tree cover in the geospatial image to generate a tree cover-augmented geospatial image, wherein the simulated tree cover partially occludes a rooftop of the building as depicted in the geospatial image, and training a machine learning model, using the tree cover-augmented geospatial image, to extract building rooftop polygons outlining buildings as depicted in geospatial imagery.
Methods and systems for generating artificial parcel data are provided. An example method involves accessing geospatial imagery that depicts at least one building and its surrounding area, applying at least one machine learning model to the geospatial imagery to generate artificial parcel data that represents a shape and location of a legal land parcel occupied by the building, accessing building footprint data comprising at least one polygon that represents a shape and location of the building, storing the artificial parcel data and the building footprint data in association with address data that represents an address of the building, receiving a query, the query comprising an address of the building or geospatial coordinates situated within the building footprint or artificial parcel data corresponding to the building, and providing, in response to the query, the building footprint data and the artificial parcel data corresponding to the building.
Methods and systems for generating artificial parcel data are provided. An example method involves accessing geospatial imagery depicting one or more buildings and surrounding areas, applying a machine learning model to the geospatial imagery to generate artificial parcel data in a form of a distance-transform raster map that represents a legal land parcel for each of the one or more buildings, and converting the distance-transform raster map into a vector map containing one or more polygons that represent the boundaries of each of the legal land parcels.
Methods and systems for generating vector maps representing features depicted in imagery are provided. An example method involves obtaining remote imagery that depicts a feature, applying a machine learning model to the remote imagery to extract a geometric model of the feature encoded as a tokenized representation of a sequence of annotation operations, and interpreting the tokenized representation as a vector map representing the geometric model of the feature.
Example systems and methods for configuring machine learning models to generate geometric models are provided. An example method involves obtaining geometric modelling data comprising sequences of geometric modelling operations, and training the machine learning model on the geometric modelling data to generate geometric models encoded as tokenized representations of sequences of geometric modelling operations to be performed to build the geometric models, wherein the machine learning model is trained to generate the geometric models in accordance with learned geometric modelling practices extracted from the geometric modelling data.
Methods and systems for generating vector maps representing features depicted in imagery are provided. An example method involves obtaining remote imagery that depicts a feature, applying a machine learning model to the remote imagery to extract a geometric model of the feature encoded as a tokenized representation of a sequence of annotation operations, and interpreting the tokenized representation as a vector map representing the geometric model of the feature.
G06V 10/766 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using regression, e.g. by projecting features on hyperplanes
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 10/77 - 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 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
Methods and systems for generating vector maps representing features depicted in imagery are provided. An example method involves obtaining remote imagery that depicts a feature, applying a machine learning model to the remote imagery to extract a geometric model of the feature encoded as a tokenized representation of a sequence of annotation operations, and interpreting the tokenized representation as a vector map representing the geometric model of the feature.
Example systems and methods for configuring machine learning models to generate geometric models are provided. An example method involves obtaining geometric modelling data comprising sequences of geometric modelling operations, and training the machine learning model on the geometric modelling data to generate geometric models encoded as tokenized representations of sequences of geometric modelling operations to be performed to build the geometric models, wherein the machine learning model is trained to generate the geometric models in accordance with learned geometric modelling practices extracted from the geometric modelling data.
42 - Scientific, technological and industrial services, research and design
Goods & Services
Analyzing and compiling business data for providing digital 2D and 3D map data; Analyzing and compiling business data for geocoding information Mapping services featuring providing digital 2D and 3D map data
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Analyzing and compiling business data for providing digital 2D and 3D map data; Analyzing and compiling business data for geocoding information
(2) Mapping services featuring providing digital 2D and 3D map data; software as a service (SASS) services featuring software for viewing 2D and 3D map data
