CNN-Based First Floor Height Detection for Flood Risk Assessment
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Solution Overview
Problem
The manual process of determining first floor height (FFH) for flood insurance is labor-intensive and costly, leading to low participation in flood insurance due to high costs, and existing technologies lack the accuracy and reliability for automated FFH determination.
Innovation Solution
A computer-based system using convolutional neural networks (CNN) processes images to detect building features and combines this with digital elevation maps and building footprints to accurately determine FFH, reducing the need for manual inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to determine first floor height, then measurement accuracy can be achieved, but the process becomes labor-intensive and costly
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated computer-based system that uses image processing and machine learning algorithms to detect and measure first floor height from images, eliminating the need for physical manual measurement while maintaining accuracy
Solution Approach 2:
The system creates a digital copy of the physical inspection process by capturing images of the building and using computational algorithms to extract measurement information from these images, replacing the need for physical presence and manual measurement tools
2Measurement precision
If manual inspection processes are required, then accurate FFH determination is possible, but flood insurance participation decreases due to high costs
Solution Approach 1:
The system enables automated self-service determination of first floor height by processing images and automatically calculating measurements without requiring manual intervention from surveyors or inspectors, making the process available at lower cost to more properties
Solution Approach 2:
By replacing expensive manual inspection services with an automated computational system, the patent reduces the cost barrier to FFH determination, making flood insurance more accessible while maintaining measurement accuracy
3Measurement precision
If traditional survey methods are used, then FFH can be determined, but the process requires physical access and manual effort
Solution Approach 1:
The patent replaces complex manual surveying equipment and procedures with a software-based image processing system that automatically extracts measurement information from images using machine learning, simplifying the overall process while maintaining precision
Data Source
AI summary
A method using a convolutional neural network to auto-determine a first floor height (FFH) and a FFH elevation (FFE) of a building. The FFH, and FFE of the building are determined with respect to the terrain or surface of the parcel of land on which the building is located. In turn, by knowing the FFH and/or FFE of the building on the parcel, it is possible to use that information while performing a flood risk assessment to a property without requiring a personal inspection of the parcel by a human.