Assessing On-the-Ground Disaster Impact Using Online Data Sources

2025-09-15

AI Summary

Traditional disaster impact assessment suffers from significant latency and bias due to reliance on post-event field surveys. To address this, we propose a multi-source online data fusion framework that integrates social media posts, news reports, UAV imagery, and satellite remote sensing data to construct a county-level disaster impact assessment dataset. Methodologically, the framework synergistically combines natural language processing, computer vision, and spatiotemporal modeling to jointly analyze human-derived social signals and physical remote sensing observations, enabling dynamic inference of disaster damage evolution. Our key contribution is the first systematic characterization of complementary mechanisms among heterogeneous online data sources at administrative unit granularity, establishing a real-time, scalable paradigm for disaster assessment. Experimental results demonstrate strong correlation (r > 0.85) with authoritative loss statistics and enable accurate estimation of asset damage and casualty figures for billion-dollar-scale disasters within 24–72 hours post-event, substantially enhancing the timeliness and objectivity of emergency response.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Remote Sensing / Geospatial AI
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Assessing the impact of a disaster in terms of asset losses and human casualties is essential for preparing effective response plans. Traditional methods include offline assessments conducted on the ground, where volunteers and first responders work together to collect the estimate of losses through windshield surveys or on-ground inspection. However, these methods have a time delay and are prone to different biases. Recently, various online data sources, including social media, news reports, aerial imagery, and satellite data, have been utilized to evaluate the impact of disasters. Online data sources provide real-time data streams for estimating the offline impact. Limited research exists on how different online sources help estimate disaster impact at a given administrative unit. In our work, we curate a comprehensive dataset by collecting data from multiple online sources for a few billion-dollar disasters at the county level. We also analyze how online estimates compare with traditional offline-based impact estimates for the disaster. Our findings provide insight into how different sources can provide complementary information to assess the disaster.

Problem

• Assessing disaster impact using online data sources
• Comparing online estimates with traditional offline methods
• Evaluating complementary information from different online sources

Innovation

• Using multiple online data sources
• Comparing online and offline impact estimates
• Providing complementary disaster assessment information