RAPIDMap: Rapid Multi-Agent Pipeline for Interpretable Disaster Mapping from Satellite and Street-view Imagery
cs.MA, cs.AI, cs.CY
Submitted: 2026-08-30
Updated: 2026-09-02
Comments: 10 pages, 7 figures, accepted by CaGIS Conference 2026, https://cartogis.org/docs/conferences/CaGIS_2026/abstracts/research/Yang_and_Zou_research_abstract_CaGIS_2026.pdf
License: http://creativecommons.org/licenses/by/4.0/
The gist: Rapid and reliable disaster mapping of impacted areas, damaged infrastructure, and affected populations is essential for emergency response and recovery.
Terminology
Abstract
Rapid and reliable disaster mapping of impacted areas, damaged infrastructure, and affected populations is essential for emergency response and recovery. However, existing AI-based approaches often require extensive manual annotation, lack cross-hazard generalization, and rely on single-modal observations. To address these challenges, this paper proposes RAPIDMap, a rapid multi-agent pipeline for zero-shot interpretable disaster mapping from satellite and street-view imagery. The framework integrates four intelligent agents: Disaster Perception Agent (DPA), Image Restoration Agent (IRA), Damage Recognition Agent (DRA), and Disaster Mapping Agent (DMA). By combining remote sensing and street-view data, RAPIDMap eliminates the need for manual fine-tuning, generalizes across multiple disaster categories, and generates structured, map-ready disaster intelligence with recovery recommendations.
Sources
- Integration of Large Vision Language Models for Efficient Post-disaster Damage Assessment and Reporting
- A Multimodal, Multilingual, and Multidimensional Pipeline for Fine-grained Crowdsourcing Earthquake Damage Evaluation
- DamageArbiter: A Multimodal Arbitration Framework for Disaster Damage Assessment from Street-View Imagery
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