AI Disaster Damage Estimation Using Segmented Aerial Imagery
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Solution Overview
Problem
Current methods for identifying and assessing disaster damage in affected areas are inefficient, relying heavily on manual labor and lacking in detail, especially when using aerial images from drones or satellites, which do not provide sufficient information for accurate damage assessment.
Innovation Solution
A method and apparatus that utilize AI to extract feature information from disaster-prone areas, identify disaster-affected areas, and estimate damage size by training learning models with labeled disaster images and related information, incorporating additional data and external sources for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual labor is used to identify disaster damage, then the process is simple to implement, but the efficiency and accuracy are low
Solution Approach 1:
The patent replaces manual labor (mechanical system) with AI-based image processing and machine learning algorithms to identify disaster-affected areas and estimate damage sizes. The system uses deep learning models that automatically analyze aerial images, extract features, and classify damaged regions, thereby significantly improving identification efficiency and accuracy while reducing human resource requirements.
Solution Approach 2:
The system enables self-service disaster assessment by automatically processing images and generating damage reports without requiring manual intervention. The AI model independently performs image analysis, area calculation, and damage estimation, allowing the system to serve itself and eliminating the need for manual surveyors to physically inspect damage sites.
2Measurement precision
If aerial images from drones or satellites are used, then the coverage area is large, but the detail information is insufficient
Solution Approach 1:
The patent segments the disaster-affected area into multiple regions of interest (ROIs) based on detected damage patterns. The system divides the large aerial image into smaller manageable segments, processes each segment through the AI model individually, and then aggregates the results. This segmentation allows the system to maintain high measurement precision by focusing computational resources on specific damaged areas while still covering the entire large region.
Solution Approach 2:
The system transitions from two-dimensional aerial images to three-dimensional analysis by incorporating elevation data, building height information, and spatial context. The AI model analyzes multiple dimensions simultaneously including spectral characteristics, spatial patterns, and temporal changes, enabling precise damage identification even in detailed regions while maintaining overall area coverage through efficient multi-scale processing.
3Measurement precision
If AI-based analysis is applied to extract feature information, then the measurement precision improves, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing aerial images to enhance relevant features and preparing training datasets with labeled damage regions before actual disaster assessment. The system pre-trains AI models on historical disaster data and pre-processes images to standard formats, so that when real disaster assessment is needed, the computational burden is reduced and measurement precision is maintained through the use of pre-optimized algorithms and pre-prepared reference data.
Data Source
AI summary
According to an embodiment of the present disclosure, there may be provided an operation method of a server for estimating the size of damage in disaster affected areas. In this instance, the operation method of the server may include acquiring at least one first disaster image, deriving an affected area from each of the at least one first disaster image, acquiring affected area related information through labeling based on the derived affected area, and training a first learning model using the at least one first disaster image and the affected area related information.