Patent ID: 11893538
Assignee: UNITED SERVICES AUTOMOBILE ASSOCIATION (USAA)
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 6:
7. A method of improving the accuracy of a damage assessment based on aerial imagery, the method comprising:
obtaining a first set of imagery of a first property captured at a first time;
clipping images in the first set of imagery to generate first clipped images, the clipping comprising:
selecting a first selected property, from properties queried for in the first set of imagery, having a point in a first footprint polygon that is found by querying a geographic information source; and
clipping images in the first set of imagery, based on a location of the first selected property, at two zooms including a neighborhood level zoom and a structure level zoom;
obtaining a second set of imagery of the first property captured at a second time subsequent to the first time, wherein a natural disaster has impacted the first property during the interval between the first time and the second time;
clipping images in the second set of imagery to generate second clipped images, the clipping comprising:
selecting a second selected property, from properties queried for in the second set of imagery, having a point in a second footprint polygon that is found by querying the geographic information source; and
clipping images in the second set of imagery, based on a location of the second selected property, at two zooms including a neighborhood level zoom and a structure level zoom;
feeding the first clipped images and the second clipped images to a deep learning damage classification model;
determining, via the deep learning classification model, that a first portion of a first structure located on the first property has been damaged, and identifying varying damage magnitude levels associated with the first portion; and
generating and presenting a first heat map that visually represents the damage magnitude levels associated with the first portion;
wherein the first set of imagery and the second set of imagery each include oblique aerial images of the first structure, and the method further comprises employing a machine learning model to evaluate the oblique aerial images and detect structural damage to sides of the first structure and to interior portions of the first structure.