Verisk Analytics has patented a system to predict structure damage post a weather event. By analyzing aerial images, weather data, and structure attributes, the system assigns a risk rating to structures in a region of interest. The data package generated is displayed on a graphical user interface for user access. GlobalData’s report on Verisk Analytics gives a 360-degree view of the company including its patenting strategy. Buy the report here.

According to GlobalData’s company profile on Verisk Analytics, was a key innovation area identified from patents. Verisk Analytics's grant share as of April 2024 was 29%. Grant share is based on the ratio of number of grants to total number of patents.

Predicting damage to structures following major weather events

Source: United States Patent and Trademark Office (USPTO). Credit: Verisk Analytics Inc

A recently granted patent (Publication Number: US11922509B2) outlines a method and system for predicting damage to a structure using a computer system. The method involves receiving a geospatial region of interest from a user, retrieving aerial images and weather data associated with the region, processing the images using a machine learning algorithm to extract structure attributes, determining the likelihood of damage based on these attributes and weather data, and transmitting this information to the user. The data is displayed in a graphical user interface, showing the geospatial region of interest on a project map with selectable layers, including weather event overlays on the property. The system includes a memory storing aerial images and a processor for executing the prediction process.

The patent claims cover various aspects of the method and system, including the indication of the geospatial region of interest, the extraction of structure attributes using machine learning, and the display of data in a graphical user interface. The system can process different types of aerial images, such as satellite or UAV images, and weather data related to events like hail storms, wind, and hurricanes. Additionally, the machine learning algorithm focuses on extracting roof-related attributes like type, area, slope, material, and eave height. The system also calculates a risk rating level correlated to the likelihood of damage and can detect, extract, and categorize structure data from various sources, including weather-related and non-weather-related events like wildfires, lightning, and arson. Overall, the patent provides a comprehensive approach to predicting damage to structures using advanced technology and data analysis methods.

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