Abstract: Computer vision systems and methods for property scene understanding from digital images, videos, media and/or sensor information are provided. The system obtains media content indicative of an asset, performs feature segmentation and material recognition, performs object detection on the features, performs hazard detection to detect one or more safety hazards, and performs damage detection to detect any visible damage, to develop a better understanding of the property using one or more features in the media content. The system can output the feature segmentation and material detection, the hazard detection, the content feature detection, and the damage detection, and all other available models to an adjuster or other user on a user interface.

Abstract: Computer vision systems and methods for hazard detection from digital images and videos are provided. The system obtains media content indicative of an asset, preprocesses the media content, and extracts, based at least in part on one or more feature extractors, features from the preprocessed media content. The system determines, based at least in part on one or more classifiers, a value associated with a hazard, which can indicate the likelihood of a media content having the hazard. The system determines that the media content includes the hazard based at least in part on a comparison of the value to a threshold value. The system can generate a visual indication in the image indicative of the hazard.

Abstract: Computer vision systems and methods for segmenting and classifying building components, contents, materials or attributes are provided. The system obtains media content indicative of an asset. The system identifies and segments items of the asset based on one or more segmentation models. The system determines, based on one or more classification models, a value associated with material or other attribute classification for each of the segmented items. The value indicates how likely the segmented item belongs to a particular material or attribute type. The system determines a material or attribute type for each of the segmented items based on a comparison of the confidence value of the material or the attribute to pre-calculated threshold values. The threshold values define a cut-off indicative of a segmented item most likely to be a particular type of material or attribute.

Abstract: Systems and methods for collaborative augmented reality measurement of an object using computing devices are provided. The system establishes an audio and video (A/V) connection between a mobile device of a first user and a remote device of a second user such that the second user can view and edit an augmented reality scene displayed on a display of the mobile device. The system receives a measurement tool selection from the first user or the second user to measure an object and/or feature present in the augmented reality scene. The system detects a plane of the augmented reality scene as a reference to position and capture points to execute a measurement of the object and/or feature. The system determines a measurement of the object and/or feature and transmits the measurement to a server.