A damage assessment (DA) computing device for determining building damage may be provided. The DA computing device may retrieve historical damage data associated with roof damage from a historical damage database, generate a damage model based upon the retrieved historical damage data, identify an building for a roof damage assessment, retrieve damage data associated with the building, compare the damage data associated with the roof to the damage model, and/or determine a damage status of the roof based upon the comparison.

Embodiments of a method and system described herein enable capture of video data streams from multiple, different video data source devices and the processing of the video data streams. The video data streams are merged such that various data protocols can all be processed with the same worker processors on different types of operating systems, which are typically distributed. In an embodiment the multiple video data sources comprises at least one mobile device executing a video sensing application that produces a video data stream for processing by video analysis worker processes. The processes include automatically detecting features in an urban scene comprising sidewalks, roads, bike routes and road crossings.

The present invention relates to a method and system for facilitating access to recorded data. The system comprises an interface and a processing device. The interface is arranged to receive data and the processing device is arranged to separate the received data in data subsets, compress each data subset and assign an identifier to each compressed data subset, thereby creating data units each comprising a compressed data subset and an associated identifier, the processing device further being arranged to establish an index on the basis of the assigned identifiers.

A system for detecting and describing keypoints in images is described. A camera is configured to capture an image including a plurality of pixels. A fully convolutional network is configured to jointly and concurrently: generate descriptors for each of the pixels, respectively; generate reliability scores for each of the pixels, respectively; and generate repeatability scores for each of the pixels, respectively. A scoring module is configured to generate scores for the pixels, respectively, based on the reliability scores and the repeatability scores of the pixels, respectively. A keypoint list module is configured to: select X of the pixels having the X highest scores, where X is an integer greater than 1; and generate a keypoint list including: locations of the selected X pixels; and the descriptors of the selected X pixels.

An Artificial Intelligence (AI) roof deterioration analysis system that tracks changes in roofs over time by evaluating a series of features in high-resolution images.

The present invention relates to a method and system for facilitating access to recorded data. The system comprises an interface and a processing device. The interface is arranged to receive data and the processing device is arranged to separate the received data in data subsets, compress each data subset and assign an identifier to each compressed data subset, thereby creating data units each comprising a compressed data subset and an associated identifier, the processing device further being arranged to establish an index on the basis of the assigned identifiers.

A technique is introduced for utilizing data associated with a monitored premises to determine a likelihood of a crime, or other activity, occurring at the premises. In an example embodiment, premises data is received from one or more sources including sensor devices located at the premises and other data sources including third-party databases. The premises data is processed using a machine learning model, such as an artificial neural network, to generate a risk score that is indicative of the likelihood of a crime occurring at the premises in real-time or in the future. The introduced technique for risk evaluation can be implemented in conjunction with a premises security system, for example, to route alarms generated by monitoring devices located at the premises.

Methods, systems, and computer code on computer-readable media are provided that are directed to generating area skyline data using a digital elevation or surface models (DEMs or DESs) and shadow casting techniques. Some embodiments use an area of maximum shadow line overlap, for shadow line images based on target location skyline azimuth and elevation angle data, as the best approximation for the position of the target location in an area. Some embodiments select the location showing the best fit to the target skyline azimuth and elevation angle data as the best approximation for the target location.

A damage assessment (DA) computing device for determining building damage may be provided. The DA computing device may train a machine learning damage model using historical damage data, identify a plurality of buildings that are susceptible to potential damage from an upcoming weather event, the plurality of buildings including the building, input data associated with a roof of the building to the trained damage model, receive a model output from the trained damage model, the model output including a damage status of the roof representing the predicted extent of damage to the roof, when the predicted extent of damage to the roof exceeds a threshold, automatically generate a claim initiation message including a link that, upon selection thereof, causes initiation an insurance claim for the roof based upon the model output and the parameters associated with the building, and/or transmit the claim initiation message to a user.

A system, method, and computer-readable storage medium are disclosed that execute machine vision operations to categorize a locality. At least one embodiment accesses a map image of a locality, where the map image includes geographical artefacts corresponding to entities within the locality; analyzes the map image to detect the entities in the locality using the geographical artefacts; assigns entity classes to detected entities in the locality; assigns a locality score to the locality based on entity classes included in the locality; retrieves street view images for one or more of the detected entities in the locality; and analyzes street view images of the detected entities to assign one or more further classifications to the detected entities. Other embodiments include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.