RAPIDMap: Rapid Multi-Agent Pipeline for Interpretable Disaster Mapping from Satellite and Street-view Imagery

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.

Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)

I.4.8; I.2.11; J.2

arXiv:2609.00046 [cs.MA]

(or arXiv:2609.00046v1 [cs.MA] for this version)

https://doi.org/10.48550/arXiv.2609.00046 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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