Drone Damage Mapping With AI-Generated 3D Disaster Assessment
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Solution Overview
Problem
Existing systems struggle to quickly and accurately assess damage situations during disasters, hindering efficient rescue and recovery operations.
Innovation Solution
A system comprising an activation unit, data collection unit, and visualization unit that utilizes drones equipped with cameras to collect and analyze damage data using generative AI, enabling real-time visualization of the damage situation as a 3D map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to assess damage situations, then the assessment can be performed with simple equipment, but the speed and accuracy of damage assessment deteriorates
Solution Approach 1:
The patent transitions from 2D aerial photography to 3D modeling of damage situations. By constructing three-dimensional models of buildings and infrastructure from aerial images, the system provides comprehensive spatial information about damage extent, structural integrity, and affected areas, significantly improving assessment accuracy and enabling faster decision-making
Solution Approach 2:
The patent introduces AI technology as an intermediary between data collection and damage assessment. The AI system automatically analyzes aerial images, identifies damage patterns, and generates 3D models, eliminating the need for manual assessment and dramatically reducing assessment time while maintaining high accuracy
2Measurement precision
If detailed damage assessment is performed manually, then assessment accuracy improves, but the time required and operational complexity increases
Solution Approach 1:
The patent implements an automated system where the AI technology performs self-service by automatically analyzing aerial images, detecting damage, and generating 3D models without human intervention. This eliminates the need for complex manual assessment procedures while maintaining high detection accuracy
Solution Approach 2:
The patent replaces manual mechanical assessment processes with automated AI-based image analysis. The system uses computer vision and machine learning algorithms to automatically identify and classify damage, substituting human labor and complex manual procedures with intelligent automated systems
3Area of stationary object
If comprehensive damage data is collected across large areas, then assessment coverage improves, but the time and resources required increase
Solution Approach 1:
The patent performs preliminary action by conducting aerial surveys and collecting images of large areas before detailed analysis begins. The system captures comprehensive coverage data in advance, allowing subsequent AI processing to efficiently analyze the information without requiring additional time-consuming field operations
Solution Approach 2:
The patent uses aerial photography to capture wide-area coverage from an elevated perspective, enabling comprehensive assessment of large regions in a single pass. The 3D modeling further enhances this by providing volumetric information about damage across the entire assessed area, maximizing coverage efficiency
Data Source
AI summary
The system according to the embodiment comprises an activation unit, a data collection unit, an analysis unit, and a visualization unit. The activation unit activates a drone. The data collection unit processes data collected by the drone activated by the activation unit. The analysis unit analyzes the data collected by the data collection unit. The visualization unit visualizes the data analyzed by the analysis unit.