Abstract: Systems and methods are disclosed for directed image capture of a subject of interest, such as a home. Directed image capture can produce higher quality images such as more centrally located within a display and/or viewfinder of an image capture device, higher quality images have greater value for subsequent uses of captured images such as for information extraction or model reconstruction. Graphical guide(s) facilitate content placement for certain positions and quality assessments for the content of interest can be calculated such as for pixel distance of the content of interest to a centroid of the display or viewfinder, or the effect of obscuring objects. Quality assessments can further include instructions for improving the quality of the image capture for the content of interest.

Abstract: Distance measurements are received from one or more distance measurement sensors, which may be coupled to a vehicle. A three-dimensional (3D) point cloud are generated based on the distance measurements. In some cases, 3D point clouds corresponding to distance measurements from different distance measurement sensors may be combined into one 3D point cloud. A voxelized model is generated based on the 3D point cloud. An object may be detected within the voxelized model, and in some cases may be classified by object type. If the distance measurement sensors are coupled to a vehicle, the vehicle may avoid the detected object.

Abstract: Distance measurements are received from one or more distance measurement sensors, which may be coupled to a vehicle. A three-dimensional (3D) point cloud are generated based on the distance measurements. In some cases, 3D point clouds corresponding to distance measurements from different distance measurement sensors may be combined into one 3D point cloud. A voxelized model is generated based on the 3D point cloud. An object may be detected within the voxelized model, and in some cases may be classified by object type. If the distance measurement sensors are coupled to a vehicle, the vehicle may avoid the detected object.

Abstract: Systems and methods for data visualization and network extraction in accordance with embodiments of the invention are illustrated. One embodiment includes a method including obtaining a graph comprising a plurality of nodes and a plurality of edges, identifying a plurality of communities in the graph, where each community includes nodes from the plurality of nodes, generating a community graph structure based on the identified communities, where the community graph includes a plurality of supernodes and a plurality of superedges, spatializing the community graph structure, unpacking the spatialized community graph structure into an unpacked graph structure comprising the plurality of nodes and the plurality of edges, where each node in the plurality of nodes is located at approximately the position of the supernode that represented it, spatializing the unpacked graph structure, and providing the spatialized unpacked graph structure.

Abstract: Systems and methods are disclosed for directed image capture of a subject of interest, such as a home. Directed image capture can produce higher quality images such as more centrally located within a display and/or viewfinder of an image capture device, higher quality images have greater value for subsequent uses of captured images such as for information extraction or model reconstruction. Graphical guide(s) facilitate content placement for certain positions and quality assessments for the content of interest can be calculated such as for pixel distance of the content of interest to a centroid of the display or viewfinder, or the effect of obscuring objects. Quality assessments can further include instructions for improving the quality of the image capture for the content of interest.

Abstract: A system and method is provided for measurements of building façade elements by combining ground-level and orthogonal imagery. The measurements of the dimension of building façade elements are based on ground-level imagery that is scaled and geo-referenced using orthogonal imagery. The method continues by creating a tabular dataset of measurements for one or more architectural elements such as siding (e.g., aluminum, vinyl, wood, brick and/or paint), windows or doors. The tabular dataset can be part of an estimate report.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.

Abstract: A system and method is provided for measurements of building façade elements by combining ground-level (201) and orthogonal imagery (906). The measurements of the dimension of building façade elements are based on ground-level imagery that is scaled and geo-referenced using orthogonal imagery (209). The method continues by creating a tabular dataset (1002) of measurements for one or more architectural elements such as siding (e.g., aluminum, vinyl, wood, brick and/or paint), windows or doors. The tabular dataset can be part of an estimate report (1002).

Abstract: A disambiguation system for an autonomous vehicle is described herein that disambiguates a traffic light framed by a plurality of regions of interest. The autonomous vehicle includes a localization system that defines the plurality of regions of interest around traffic lights captured in a sensor signal and provides an input to a disambiguation system. When the captured traffic lights are in close proximity, the plurality of regions of interest overlap each other such that a traffic light disposed in the overlapping region is ambiguous to an object detector because it is framed by more than one region of interest. A disambiguation system associates the traffic light with the correct region of interest to disambiguate a relationship thereof and generates a disambiguated directive for controlling the autonomous vehicle. Disambiguation can be achieved according to any of an edge-based technique, a vertex-based technique, and a region of interest distance-based technique.

Abstract: An autonomous vehicle incorporating a multimodal multi-technique signal fusion system is described herein. The signal fusion system is configured to receive at least one sensor signal that is output by at least one sensor system (multimodal), such as at least one image sensor signal from at least one camera. The at least one sensor signal is provided to a plurality of object detector modules of different types (multi-technique), such as an absolute detector module and a relative activation detector module, that generate independent directives based on the at least one sensor signal. The independent directives are fused by a signal fusion module to output a fused directive for controlling the autonomous vehicle.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.

Abstract: An autonomous vehicle incorporating a multimodal multi-technique signal fusion system is described herein. The signal fusion system is configured to receive at least one sensor signal that is output by at least one sensor system (multimodal), such as at least one image sensor signal from at least one camera. The at least one sensor signal is provided to a plurality of object detector modules of different types (multi-technique), such as an absolute detector module and a relative activation detector module, that generate independent directives based on the at least one sensor signal. The independent directives are fused by a signal fusion module to output a fused directive for controlling the autonomous vehicle.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.

Abstract: Systems and methods are disclosed for directed image capture of a subject of interest, such as a home. Directed image capture can produce higher quality images such as more centrally located within a display and/or viewfinder of an image capture device, higher quality images have greater value for subsequent uses of captured images such as for information extraction or model reconstruction. Graphical guide(s) facilitate content placement for certain positions and quality assessments for the content of interest can be calculated such as for pixel distance of the content of interest to a centroid of the display or viewfinder, or the effect of obscuring objects. Quality assessments can further include instructions for improving the quality of the image capture for the content of interest.

Abstract: A process is provided for graphically guiding a user of a capture device (e.g., smartphone) to more accurately capture a series of images of a building. Images are captured as the picture taker moves around the building—taking a plurality (e.g., 4-16) of images from multiple angles and distances. Before capturing an image, a quality of the image may be determined to prevent low quality images from being captured or to provide instructions on how to improve the quality of the image capture. The series of captured images are uploaded to an image processing system to generate a 3D building model that is returned to the user. The returned 3D building model may incorporate scaled measurements of building architectural elements and may include a dataset of measurements for one or more architectural elements such as siding (e.g., aluminum, vinyl, wood, brick and/or paint), windows, doors or roofing.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.

Abstract: An autonomous vehicle incorporating a multimodal multi-technique signal fusion system is described herein. The signal fusion system is configured to receive at least one sensor signal that is output by at least one sensor system (multimodal), such as at least one image sensor signal from at least one camera. The at least one sensor signal is provided to a plurality of object detector modules of different types (multi-technique), such as an absolute detector module and a relative activation detector module, that generate independent directives based on the at least one sensor signal. The independent directives are fused by a signal fusion module to output a fused directive for controlling the autonomous vehicle.

Abstract: A disambiguation system for an autonomous vehicle is described herein that disambiguates a traffic light framed by a plurality of regions of interest. The autonomous vehicle includes a localization system that defines the plurality of regions of interest around traffic lights captured in a sensor signal and provides an input to a disambiguation system. When the captured traffic lights are in close proximity, the plurality of regions of interest overlap each other such that a traffic light disposed in the overlapping region is ambiguous to an object detector because it is framed by more than one region of interest. A disambiguation system associates the traffic light with the correct region of interest to disambiguate a relationship thereof and generates a disambiguated directive for controlling the autonomous vehicle. Disambiguation can be achieved according to any of an edge-based technique, a vertex-based technique, and a region of interest distance-based technique.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.

Abstract: Systems and methods for locating telecommunication cell sites in accordance with embodiments of the invention are illustrated. One embodiment includes a method for locating cell sites, including obtaining a plurality of observations, where each observation includes a timestamp, a coordinate, an active record, and a set of passive records, uniquely identifying secondary cell sites in the passive records by cross-matching active records from a first observation with passive records from a second observation, annotating the observations with unique identifiers for each secondary cell site, time-smoothing the received signal strength values, estimating the distance from each observation to the primary cell site and secondary cell sites associated with the observation by providing a machine learning model with at least the time-smoothed signal strength values and the plurality of annotated observations, and locating the primary cell sites based on the estimated distances.