Aerial Structure Damage Detection with Weather-Based Risk Rating
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Solution Overview
Problem
Existing systems struggle to accurately detect and categorize structure damage from major weather events, leading to incomplete understanding of damage assessment and inefficient resource allocation.
Innovation Solution
A system that processes digital images and weather data to automatically detect, extract, and categorize structure data using machine learning algorithms, assigning risk ratings and generating data packages with visualization tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional software systems are used to process aerial images, then basic structure detection is possible, but the accuracy of damage likelihood determination is insufficient
Solution Approach 1:
The patent combines multiple data sources (aerial imagery, weather event data, structure attributes) and multiple analysis functions (damage detection, risk assessment, categorization) into a single integrated system. This merging enables comprehensive damage assessment by correlating weather data with structure characteristics and visual damage evidence, thereby improving both accuracy and reliability simultaneously.
Solution Approach 2:
The system performs multiple functions including damage detection, structure attribute extraction, weather data correlation, risk rating assignment, and damage categorization within a single platform. This multi-functionality allows the system to provide comprehensive damage assessments that are both accurate and complete, resolving the contradiction between measurement precision and reliability.
2Loss of information
If comprehensive structure data extraction is performed on all properties in a region, then complete damage understanding is achieved, but the complexity of processing and categorizing large volumes of data increases
Solution Approach 1:
The patent segments the damage assessment process into distinct functional modules: aerial imagery processing, weather data integration, structure attribute extraction, damage detection, risk rating assignment, and damage categorization. This segmentation reduces system complexity by organizing complex data processing tasks into manageable, independent components while maintaining comprehensive information extraction.
Solution Approach 2:
The system applies different processing approaches to different structures based on their specific attributes and exposure to weather events. By customizing the analysis depth and categorization criteria for each structure type and location, the system maintains complete damage information while avoiding unnecessary processing complexity for structures with lower risk or similar characteristics.
3Measurement precision
If detailed analysis of each structure is performed to accurately assess damage, then assessment accuracy improves, but the time required for processing increases
Solution Approach 1:
The system performs preliminary processing of aerial imagery and extraction of structure attributes before the actual damage assessment. By pre-processing and organizing data in advance, the system reduces the time required for detailed analysis while maintaining assessment accuracy. Weather data is also integrated in advance to enable rapid correlation during the assessment phase.
Solution Approach 2:
The system applies full detailed analysis to structures with high exposure to weather events and those showing significant damage indicators, while using streamlined assessment for structures with minimal exposure or no visible damage. This selective approach maintains accuracy for critical cases while reducing overall processing time.
Data Source
AI summary
Systems and methods for detecting, extracting, and categorizing structure data from aerial imagery following a major weather event are provided. The system processes digital images and weather data to automatically detect, extract, and categorize structure data following a major weather event. After receiving an indication of a region of interest (“ROI”) from a user, the system retrieves weather mapping data for the ROI and retrieves information related to attributes of structures within the ROI from a machine learning subsystem. The system then cross-references the property data, the weather data, and the structure attributes and assigns a risk rating to the structures within the ROI. Finally, the system generates and delivers a data package to the user.