Abstract: A system for modeling a roof of a structure comprising a first database, a second database and a processor in communication with the first database and the second database. The processor selects one or more images and the respective metadata thereof from the first database based on a received a geospatial region of interest. The processor generates two-dimensional line segment geometries in pixel space based on two-dimensional outputs generated by a neural network in pixel space of at least one roof structure present in the selected one or more images. The processor classifies the generated two-dimensional line segment geometries into at least one contour graph based on three-dimensional data received from the second database and generates a three-dimensional representation of the at least one roof structure based on the at least one contour graph and the received three-dimensional data.

Abstract: A system for modeling a roof structure comprising an aerial imagery database and a processor in communication with the aerial imagery database. The aerial imagery database stores a plurality of stereoscopic image pairs and the processor selects at least one stereoscopic image pair among the plurality of stereoscopic image pairs and related metadata from the aerial imagery database based on a geospatial region of interest. The processor identifies a target image and a reference image from the at least one stereoscopic pair and calculates a disparity value for each pixel of the identified target image to generate a disparity map. The processor generates a three dimensional point cloud based on the disparity map, the identified target image and the identified reference image. The processor optionally generates a texture map indicative of a three-dimensional representation of the roof structure based on the generated three dimensional point cloud.

Abstract: Systems and methods for automatically detecting, classifying, and processing objects captured in an images or videos are provided. In one embodiment, the system receives an image from an image source and detects one or more objects in the image. The system performs a high-level classification of the one or more objects in the image. The system performs a specific classification of the one or more objects, determines a price of the one or more objects, and generates a pricing report comprising a price of the one or more objects. In another embodiment, the system captures at least one image or video frame and classifies an object present in the image or video frame using a neural network. The system adds the classified object and an assigned object code to an inventory and processes the inventory to assign the classified object a price.

Abstract: A system and method for automatically detecting, classifying, and processing objects captured in an image. The system receives an image from the image source and detects one or more objects in the image. The system then performs a high-level classification of each of the one or more objects in the image and extracts each of the one or more objects from the image. The system then performs a specific classification of each of the one or more objects and determines a price of each of the one or more objects. Finally, the system generates a pricing report comprising a price of each of the one or more objects.

Abstract: Computer vision systems and methods for determining roof shapes from imagery using segmentation networks are provided. The system obtains an image of a structure from an image database, and determines a flat roof structure ratio and a sloped roof structure ratio of the roof structure using a neural network. Based on segmentation processing by the neural network, the system determines a flat roof structure ratio and a sloped roof structure ratio based on a portion of the roof structure classified as being flat and a portion of the roof structure classified as being sloped. Then, the system determines a ratio of each shape type of the roof structure using a neural network. The system generates a roof structure shape report indicative of a predominant shape of the roof structure and ratios of each shape type of the roof structure.

Abstract: Systems and methods for property feature detection and extraction using digital images. The image sources could include aerial imagery, satellite imagery, ground-based imagery, imagery taken from unmanned aerial vehicles (UAVs), mobile device imagery, etc. The detected geometric property features could include tree canopy, pools and other bodies of water, concrete flatwork, landscaping classifications (gravel, grass, concrete, asphalt, etc.), trampolines, property structural features (structures, buildings, pergolas, gazebos, terraces, retaining walls, and fences), and sports courts. The system can automatically extract these features from images and can then project them into world coordinates relative to a known surface in world coordinates (e.g., from a digital terrain model).

Abstract: Systems and methods for automatically detecting, classifying, and processing objects captured in an images or videos are provided. In one embodiment, the system receives an image from an image source and detects one or more objects in the image. The system performs a high-level classification of the one or more objects in the image. The system performs a specific classification of the one or more objects, determines a price of the one or more objects, and generates a pricing report comprising a price of the one or more objects. In another embodiment, the system captures at least one image or video frame and classifies an object present in the image or video frame using a neural network. The system adds the classified object and an assigned object code to an inventory and processes the inventory to assign the classified object a price.

Abstract: A system for detecting and extracting a ground surface condition from an image comprising a memory and a processor in communication with the memory. The processor performs a high resolution scan of at least one input image and generates an orthomosaic model and a digital surface model based on the performed high resolution scan. The processor generates an image tile based on the generated models and determines a label indicative of a probability of a presence of a ground surface condition for each pixel of the generated image tile via a computer vision model. The processor generates a label tensor for the at least one input image based on the determined labels and extracts a two-dimensional geospatial representation of a detected ground surface condition based on the generated label tensor. The processor generates a report indicative of damage associated with the detected ground surface condition based on the extracted two-dimensional geospatial representation.

Abstract: Computer vision systems and methods for determining roof shapes from imagery using segmentation networks are provided. The system obtains an image of a structure from an image database, and determines a flat roof structure ratio and a sloped roof structure ratio of the roof structure using a neural network. Based on segmentation processing by the neural network, the system determines a flat roof structure ratio and a sloped roof structure ratio based on a portion of the roof structure classified as being flat and a portion of the roof structure classified as being sloped. Then, the system determines a ratio of each shape type of the roof structure using a neural network. The system generates a roof structure shape report indicative of a predominant shape of the roof structure and ratios of each shape type of the roof structure.

Abstract: Computer vision systems and methods for determining structure features from point cloud data using neural networks are provided. The system obtains point cloud data of a structure or a property parcel having a structure present therein from a database. The system can preprocess the obtained point cloud data to generate another point cloud or 3D representation derived from the point cloud data by spatial cropping and/or transformation, down sampling, up sampling, and filtering. The system can also preprocess point features to generate and/or obtain any new features thereof. Then, the system extracts a structure and/or feature of the structure from the point cloud data utilizing one or more neural networks. The system determines at least one attribute of the extracted structure and/or feature of the structure utilizing the one or more neural networks.

Abstract: A system and method for generating a parametric model of a roof structure comprising a processor in communication with a memory. The system receives a plurality of parameters of each roof component composing the roof structure and performs a geometry creation based on the received plurality of parameters. The system generates a constrained three-dimensional geometry based on an output of the geometry creation, and displays a three-dimensional model of the roof structure based on the constrained three-dimensional geometry.

Abstract: Computer vision systems and methods for determining roof shapes from imagery using segmentation networks are provided. The system obtains an image of a structure from an image database, and determines a flat roof structure ratio and a sloped roof structure ratio of the roof structure using a neural network. Based on segmentation processing by the neural network, the system determines a flat roof structure ratio and a sloped roof structure ratio based on a portion of the roof structure classified as being flat and a portion of the roof structure classified as being sloped. Then, the system determines a ratio of each shape type of the roof structure using a neural network. The system generates a roof structure shape report indicative of a predominant shape of the roof structure and ratios of each shape type of the roof structure.

Abstract: Systems and methods for roof area and slope estimation using a point set are provided. The system selects roof structure points having a high probability of being positioned on a top surface of a structure present in the region of interest point set. Then, the system determines a footprint of the structure associated with the selected roof structure points. The system determines a distribution of the slopes of the roof structure points and generates a slope distribution report indicative of prominent slopes of the roof structure and each slope's contribution toward (percentage composition of) the total roof structure. The system then determines an area of the roof structure based on the footprint of the structure and the slope distribution report.

Abstract: A system and method for generating a parametric model of a roof structure comprising a processor in communication with a memory. The system receives a plurality of parameters of each roof component composing the roof structure and performs a geometry creation based on the received plurality of parameters. The system generates a constrained three-dimensional geometry based on an output of the geometry creation, and displays a three-dimensional model of the roof structure based on the constrained three-dimensional geometry.

Abstract: Computer vision systems and methods for determining roof conditions from imagery using segmentation networks are provided. The system obtains at least one image from an image database having a roof structure present therein, and determines a footprint of the roof structure using a neural network. Based on segmentation processing by the neural network, the system generates a single channel image that maps each pixel in the at least one image to a binary classification indicative of whether each pixel is or is not representative of a roof structure and executes a contour extraction algorithm on the single channel image to determine the footprint of the roof structure.

Abstract: A computer vision system and method for detecting and modeling features of a building in a plurality of images is provided. The system includes at least one computer system in communication with a database of aerial imagery, and computer vision system code executed by the at last one computer system which automatically detects contours and infers interior roof features of the building. The system first processes the plurality of images to identify a plurality of two-dimensional (2D) line segments in each image. Then, the system processes the plurality of 2D line segments to generate a plurality of three-dimensional (3D) line segments. The plurality of 2D line segments are then processed to detect a contour of the structure, and the contour of the structure is utilized by the system to infer interior roof lines from the structure. A model of the roof of the structure is finally generated using the detected contour and interior roof lines.

Abstract: A system for modeling a roof structure comprising an aerial imagery database and a processor in communication with the aerial imagery database. The aerial imagery database stores a plurality of stereoscopic image pairs and the processor selects at least one stereoscopic image pair among the plurality of stereoscopic image pairs and related metadata from the aerial imagery database based on a geospatial region of interest. The processor identifies a target image and a reference image from the at least one stereoscopic pair and calculates a disparity value for each pixel of the identified target image to generate a disparity map. The processor generates a three dimensional point cloud based on the disparity map, the identified target image and the identified reference image. The processor optionally generates a texture map indicative of a three-dimensional representation of the roof structure based on the generated three dimensional point cloud.

Abstract: Systems and methods for generating property data packages from light detection and ranging (“LIDAR”) point clouds are provided. Data from the LIDAR systems is stored in a LIDAR database in communication with a computer processor that executes computer vision system code including a 3D point cloud model generator, a measurement tool(s) subsystem, a 3D wireframe model generator, and a measurement extraction subsystem. The system receives an indication of a region of interest (“ROI”) from a user, retrieves point cloud data associated with the ROI from the LIDAR database, generates a 3D point cloud model based on the point cloud data, generates a 3D wireframe model based on the 3D point cloud model and operator input, extracts measurement information from the 3D wireframe model, and transmits a data package to the user. The data package can include, but is not limited to, the 3D point cloud model, the 3D wireframe model, and the extracted measurement data.

Abstract: Systems and methods for property feature detection and extraction using digital images. The image sources could include aerial imagery, satellite imagery, ground-based imagery, imagery taken from unmanned aerial vehicles (UAVs), mobile device imagery, etc. The detected geometric property features could include tree canopy, pools and other bodies of water, concrete flatwork, landscaping classifications (gravel, grass, concrete, asphalt, etc.), trampolines, property structural features (structures, buildings, pergolas, gazebos, terraces, retaining walls, and fences), and sports courts. The system can automatically extract these features from images and can then project them into world coordinates relative to a known surface in world coordinates (e.g., from a digital terrain model).

Abstract: A computer vision system and method for detecting and modeling features of a building in a plurality of images is provided. The system includes at least one computer system in communication with a database of aerial imagery, and computer vision system code executed by the at last one computer system which automatically detects contours and infers interior roof features of the building. The system first processes the plurality of images to identify a plurality of two-dimensional (2D) line segments in each image. Then, the system processes the plurality of 2D line segments to generate a plurality of three-dimensional (3D) line segments. The plurality of 2D line segments are then processed to detect a contour of the structure, and the contour of the structure is utilized by the system to infer interior roof lines from the structure. A model of the roof of the structure is finally generated using the detected contour and interior roof lines.