Crowd-Sourced Imagery Analysis for Post-Disaster Condition Mapping
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Solution Overview
Problem
Existing disaster response systems face challenges in effectively assessing post-disaster conditions, particularly in detecting power outages and water contamination, and in efficiently gathering data from disaster areas due to limitations in existing technologies.
Innovation Solution
A post-disaster condition monitoring system that utilizes crowd-sourced imagery, drones, and pre-existing networks to collect and analyze data on power outages and water contamination, providing a comprehensive map of conditions and enabling users to upload images or volunteer their drones for data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional disaster assessment methods are used, then data collection can be performed with existing infrastructure, but the accuracy and completeness of post-disaster condition detection is insufficient
Solution Approach 1:
The patent combines multiple data sources including crowd-sourced imagery from personal devices, drone-collected aerial imagery, and pre-existing network data into a unified disaster assessment system. This integration allows the system to achieve comprehensive and accurate detection of post-disaster conditions by merging complementary information from diverse sources, thereby improving measurement precision without requiring a single complex detection device
Solution Approach 2:
The system employs multi-functional platforms that can perform various assessment tasks. Personal electronic devices serve both as communication tools and imagery collection devices; drones perform both aerial photography and terrain mapping; the centralized server handles data aggregation, analysis, and dissemination. This multi-functionality improves detection accuracy while avoiding the need for specialized single-purpose equipment for each function
2Loss of information
If comprehensive data collection from disaster areas is attempted, then complete assessment information can be obtained, but the time required for data gathering and analysis increases
Solution Approach 1:
The system performs preliminary data collection and processing actions before complete disaster assessment is needed. Pre-existing network data is collected and stored in advance; personal devices are pre-configured with assessment applications; drones are pre-positioned or quickly deployable. When a disaster occurs, this pre-prepared infrastructure enables rapid data gathering and reduces the time needed for comprehensive assessment
Solution Approach 2:
The system maintains continuous data collection and processing operations. The centralized server continuously receives and analyzes imagery data from multiple sources; the system operates continuously to update disaster condition assessments as new information becomes available. This continuous operation ensures complete information gathering without significant delays, as the system is already active and processing when additional data is needed
3Measurement precision
If professional assessment teams are deployed to disaster areas, then accurate on-site evaluation can be performed, but the cost and logistical complexity increase
Solution Approach 1:
The system enables self-service disaster assessment by allowing local residents to use their personal electronic devices to capture and submit imagery of disaster conditions. Community members independently perform data collection tasks that would traditionally require professional teams, thereby maintaining assessment accuracy while eliminating the need for complex professional team deployment and reducing operational complexity
Solution Approach 2:
The centralized server acts as an intermediary that receives, validates, and processes imagery data from numerous individual sources. This intermediary infrastructure performs the complex tasks of data aggregation, quality control, and analysis that would otherwise require professional assessment teams, thereby simplifying individual user operations while maintaining overall assessment accuracy
4Reliability
If multiple data sources are integrated for comprehensive assessment, then detection reliability improves, but the system complexity and data processing requirements increase
Solution Approach 1:
The system segments the disaster assessment function across multiple independent data sources rather than relying on a single complex system. Imagery data is collected separately from personal devices, drones, and pre-existing networks; each source operates independently and contributes specific types of information. The centralized server then integrates these segmented data streams, improving detection reliability through diversity while avoiding the complexity of a monolithic system
Data Source
AI summary
A post-disaster conditions monitoring system. The system may include a device processor; and a non-transitory computer readable medium including instructions executable by the device processor to perform the following steps: receiving a plurality of images from a plurality of personal devices in a geographic region; determining conditions in a geographic region based on the information received from the plurality of images; and preparing a map indicating the determined conditions within the geographic region.