CoreLogic. has patented a method utilizing a convolutional neural network to automatically determine the first floor height (FFH) and elevation (FFE) of buildings. This technology enables flood risk assessments without requiring human inspections, leveraging digital surface models and overhead images for accurate measurements. GlobalData’s report on CoreLogic gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on CoreLogic, Cloud gaming was a key innovation area identified from patents. CoreLogic's grant share as of July 2024 was 55%. Grant share is based on the ratio of number of grants to total number of patents.
Automated determination of building first floor height using ai
The patent US12056611B2 outlines a computer-implemented method for detecting the first floor height (FFH) of a building using a convolutional neural network (CNN)-based artificial intelligence (AI) engine. The method begins by extracting digital surface model (DSM) information, which includes surface elevation data of the building's location. An overhead image of the building is then analyzed by the trained CNN engine, which has previously processed images of various other buildings. The CNN engine employs a data extraction network that includes feature extraction, region-of-interest (ROI) pooling, and data vectorization layers to identify the first floor from the image. The FFH is estimated by determining the roof elevation from the DSM data and calculating the height differential between the roof and the first floor.
Additionally, the method allows for the integration of interior property information from databases such as multiple listing services (MLS). This information can include room dimensions, number of floors, and structural details. The CNN engine can also be trained with various images, including interior spaces, to enhance its accuracy in identifying features such as appliances and interior structures. The method further includes steps for converting the FFH to a first floor elevation (FFE) and identifying the building's height based on the FFE. Overall, the patent presents a comprehensive approach to accurately determining the first floor height of buildings through advanced image analysis and machine learning techniques.
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