Abstract: A system and method for creating customized insurance-related forms (e.g., agreements, policies, exceptions, and other insurance-related documents) using computing devices is disclosed. A user can quickly and conveniently create customized insurance-related forms, and can transmit same to one or more remote users and/or computing systems for further processing. The system provides rich tagged searching and editing features for allowing an insurance professional to rapidly create customized forms, and to access other forms while editing a given form. The system ingests and decomposes insurance-related forms into clauses, applies search tags to the clauses, and stores the clauses and the search tags in a database for rapid searching and retrieval of desired tags for use in creating customized forms.

Abstract: Computer vision systems and methods for vehicle damage detection are provided. An embodiment of the system generates a dataset and trains a neural network with a plurality of images of the dataset to learn to detect an attribute of a vehicle present in an image of the dataset and to classify at least one feature of the detected attribute. The system can detect the attribute of the vehicle and classify the at least one feature of the detected attribute by the trained neural network. In addition, an embodiment of the system utilizes a neural network to reconstruct a vehicle from one or more digital images.

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 system and method for flight planning for an unmanned aircraft. The system generates an aerial imagery map of a capture area and determines a footprint of a structure present in the capture area by marking the structure. The system determines a difference between a takeoff elevation of the unmanned aircraft and a predetermined elevation above a center of the structure and calibrates the difference between the takeoff elevation of the unmanned aircraft and the predetermined elevation above the center of the structure. The system determines, based on the calibration, a flight path elevation of the unmanned aircraft to capture images of the structure. The system generates a flight plan based on criteria for capturing the images of the structure and executes the flight plan.

Abstract: Systems and methods for automated vehicle routing using column generation optimization are provided. The system receives capacitated vehicle routing problem (CVRP) input data and generates a minimum weight set cover problem formulation for a CVRP for performing column generation optimization over the input data. The system determines smooth-dual optimal inequalities (S-DOI) and flexible-dual optimal inequalities (F-DOI) for the CVRP for performing the column generation optimization over a valid subset of the input data. Then, the system adapts the S-DOI and the F-DOI to generate smooth and flexible dual optimal inequalities (SF-DOI) for the CVRP for performing the column generation optimization over a relaxed subset of the input data. The system utilizes the SF-DOI to accelerate column generation optimization over the relaxed subset of the input data.

Abstract: Systems and methods for dynamic computer modeling using incomplete data are provided. The system can yield a total score indicative of an accuracy and reliability of a model for a given application based on incomplete data. The system can receive one or more sets of datasets, classify each dataset among the set of datasets based on a classification component, and determine a normalized score for each dataset based on a value or values of each dataset. If a classification component does not comprise more than one dataset, then the system determines a classification component score for each dataset as the normalized score. If a classification component comprises more than one dataset, the system assigns a weighted data value to each dataset of the classification component. A classification component score is determined for each weighed dataset by applying the weighted data value to the normalized score for each weighed dataset.

Abstract: Computer vision systems and methods for image to image translation are provided. The system samples a first image and a second image of a dataset. The system utilizes a variational auto-encoder to execute a cycle consistent forward cycle and a cycle consistent reverse cycle on each of the first image and the second image to generate a disentanglement representation of the first image and a disentanglement representation of the second image, and generate a first reconstructed image and a second reconstructed image based on the first image disentanglement representation and the second image disentanglement representation.

Abstract: Computer vision systems and methods for image to image translation are provided. The system receives a first input image and a second input image and applies a content adversarial loss function to the first input image and the second input image to determine a disentanglement representation of the first input image and a disentanglement representation of the second input image. The system trains a network to generate at least one output image by applying a cross cycle consistency loss function to the first disentanglement representation and the second disentanglement representation to perform multimodal mapping between the first input image and the second input image.

Abstract: Computer vision systems and methods for compositional pixel prediction are provided. The system receives an input image frame having a plurality of entities where each entity has a location at a first time step. The system processes the input image frame to extract a representation of each entity. The system utilizes an entity predictor to determine a predicted representation of each extracted entity representation at a next time step based on each extracted entity representation and a latent variable and utilizes a frame decoder to generate a predicted frame based on the input image frame and the predicted entity representations. The system trains an encoder to predict a distribution over the latent variable based on the input image frame and a final frame of a ground truth video associated with the input image frame.

Abstract: A system and method for creating customized insurance-related forms (e.g., agreements, policies, exceptions, and other insurance-related documents) using computing devices is disclosed. A user can quickly and conveniently create customized insurance-related forms, and can transmit same to one or more remote users and/or computing systems for further processing. The system provides rich tagged searching and editing features for allowing an insurance professional to rapidly create customized forms, and to access other forms while editing a given form. The system ingests and decomposes insurance-related forms into clauses, applies search tags to the clauses, and stores the clauses and the search tags in a database for rapid searching and retrieval of desired tags for use in creating customized forms.

Abstract: A system and method for generating models from digital images in an interactive environment comprising a memory and a processor in communication with the memory. The processor captures or derives metadata for one or more digital images. The processor derives transforms from the metadata to align the digital images with one or more three-dimensional (“3D”) models of objects/structures represented in the digital image. The processor generates an interactive environment which allows a user to view a contextual model of each of the objects/structures in two dimensional (“2D”) and 3D views.

Abstract: Computer vision systems and methods for text classification are provided. The system detects a plurality of text regions in an image and generates a bounding box for each detected text region. The system utilizes a neural network to recognize text present within each bounding box and classifies the recognized text, based on at least one extracted feature of each bounding box and the recognized text present within each bounding box, according to a plurality of predefined tags. The system can associate a key with a value and return a key-value pair for each predefined tag.

Abstract: Computer vision systems and methods for optimizing correlation clustering for image segmentation are provided. The system receives input data and generates a correlation clustering formulation for Benders Decomposition for optimized correlation clustering of the input data. The system optimizes the Benders Decomposition for the generated correlation clustering formulation and performs image segmentation using the optimized Benders Decomposition.

Abstract: Computer vision systems and methods for end-to end training of neural networks are provided. The system generates a fixed point algorithm for dual-decomposition of a maximum-a-posteriori inference problem and trains the convolutional neural network and a conditional random field with the fixed point algorithm and a plurality of images of a dataset to learn to perform semantic image segmentation. The system can segment an attribute of an image of the dataset by the trained neural network and the conditional random field.

Abstract: Computer vision systems and methods for automatic parcel alignment are provided. The system receives a geotagged aerial image, parcel information, and semantic information where each of the parcel and semantic information are overlaid on the geotagged aerial image. The system cleans the parcel information and the semantic information. The system optimizes the parcel information by grouping geo-registered parcels present in the geotagged aerial image into a plurality of islands and generating a plurality of parcel alignment solutions for each island of the plurality of islands. The system refines the plurality of parcel alignment solutions for each island and regularizes each island. The system generates a composite parcel alignment solution based on the refined plurality of parcel alignment solutions for each regularized island to align the geo-registered parcels of each regularized island with the geotagged aerial image.

Abstract: Computer vision systems and methods for object detection with reinforcement learning are provided. The system includes a reinforcement learning agent configured to detect an object pertaining to a target object class and a plurality of objects pertaining to different target object classes, such that the reinforcement learning agent determines a bounding box for each of the detected of objects. The system first sets parameters of the reinforcement learning agent. The system then detects an object and/or objects in an image based on the set parameters. Finally, the system determines a bounding box and/or bounding boxes for each of the detected objects.

Abstract: Computer vision systems and methods for noisy contour alignment are provided. The system generates a loss function and trains a convolutional neural network with the loss function and a plurality of images of a dataset to learn to align contours with progressively increasing complex forward and backward transforms over increasing scales. The system can align an attribute of an image of the dataset by the trained neural network.

Abstract: Systems and methods for rapidly developing annotated computer models of structures and properties is provided. The system generates three-dimensional (3D) models of structures and property using a wide variety of digital imagery, and/or can process existing 3D models created by other systems. The system processes the 3D models to automatically identify candidate objects within the 3D models that may be suitable for annotation, such as roof faces, chimneys, windows, gutters, etc., using computer vision techniques to automatically identify such objects. Once the candidate objects have been identified, the system automatically generates user interface screens which gather relevant information related to the candidate objects, so as to rapidly obtain, associate, and store annotation information related to the candidate objects.

Abstract: Systems and methods for weather radar processing, comprising a processor in communication with a first database and a second database and computer system code executed by the processor. The computer system code causes the processor to ingest radar data from the first database and ingest numerical weather prediction data from the second database. The processor further processes the radar data and the numerical weather prediction data to generate weather products on defined tiles. The processor further processes the weather products on the defined tiles to generate full domain (stitch-tile) data and generates time aggregation data based on the stitch-tile data. The processor further generates a final model using the time aggregation data.

Abstract: Machine learning systems and methods for performing entity resolution. The system receives a dataset of observations and utilizes a machine learning algorithm to apply a blocking technique to the dataset to identify and generate a subset of pairs of observations of the dataset that could represent a same real world entity. The system generates a probability score for each pair of observations of the subset where the probability score is defined over a given pair of observations and denotes a probability that each pair is associated with a common entity in ground truth. The system utilizes a flexible minimum weight set packing framework to determine problem specific cost terms of a single hypothesis associated with the subset of pairs of observations and to perform entity resolution by partitioning the subset of pairs of observations into hypotheses based on the cost terms.