Roof Image Segmentation for Automated Hail Damage Classification
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Solution Overview
Problem
Conventional methods for assessing hail damage on properties, such as roofs, are inefficient, time-consuming, and expensive due to manual inspection and examination of image data, particularly for identifying hail damage from an entire property view without specific targeting.
Innovation Solution
A system utilizing a convolutional neural network (CNN) and classification model to analyze digital image data of properties, segmenting images into portions, identifying regions of potential hail damage, and extracting features to generate outputs indicating the presence of hail damage, thereby automating the assessment process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection by claims specialists is used to assess hail damage, then the ability to identify hail damage is maintained, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent replaces manual mechanical inspection by claims specialists with an automated image processing system that uses computer vision algorithms to detect and classify hail damage. The system automatically analyzes aerial images, segments them into regions of interest, and applies machine learning models to identify damaged areas, thereby eliminating the time-consuming manual review process while maintaining accurate damage assessment capability
Solution Approach 2:
The system enables self-service damage assessment by automatically analyzing property images without requiring human claims specialists to manually inspect each property. The automated system independently processes images, identifies hail damage patterns, and generates assessment reports, allowing the system to serve itself rather than requiring continuous human intervention for routine assessments
2Reliability
If manual examination of aerial images is used to detect property damage, then damage detection capability is maintained, but the process becomes inefficient and expensive
Solution Approach 1:
The patent replaces manual visual examination of aerial images with an automated computer vision system that processes images through multiple processing stages including image segmentation, feature extraction, and machine learning classification. This automated mechanism efficiently analyzes entire property images and identifies hail damage patterns that would be time-consuming to detect manually, significantly improving assessment productivity while maintaining reliable damage detection
Solution Approach 2:
The system divides the aerial images into multiple segments or regions of interest, allowing the processing system to analyze different portions of the property separately. This segmentation enables efficient processing of large images by breaking them into manageable units that can be evaluated for hail damage patterns, thereby improving overall assessment efficiency without compromising detection accuracy
3Productivity
If automated image analysis is implemented to assess hail damage, then assessment efficiency is improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation to divide the complex image analysis task into distinct manageable stages: image acquisition, image segmentation into regions, feature extraction from regions, and damage classification using machine learning models. This segmentation of the processing pipeline makes the overall complex system more manageable and easier to implement, as each stage can be developed and optimized independently while working together to achieve efficient automated assessment
Data Source
AI summary
Systems and methods for analyzing image data to assess property damage are disclosed. According to certain aspects, a server may analyze segmented digital image data of a roof of a property using a convolutional neural network (CNN). The server may extract a set of features from a set of regions output by the CNN. Additionally, the server may analyze the set of features using an additional image model to generate a set of outputs indicative of a confidence level that actual hail damage is depicted in the set of regions.