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

A new research framework, RAPIDMap, has been developed to enhance disaster mapping by utilizing a multi-agent pipeline for zero-shot interpretable disaster mapping. This system integrates satellite and street-view imagery to rapidly identify impacted areas, damaged infrastructure, and affected populations, addressing limitations of existing AI-based approaches such as the need for extensive manual annotation and lack of cross-hazard generalization.

Why it matters

Rapid and reliable disaster mapping is critical for enabling timely and effective emergency response, resource allocation, and recovery planning in affected regions. Technologies like RAPIDMap improve situational awareness by providing accurate damage assessments without extensive manual effort, thereby minimizing human and economic losses during crises.

Key insights

• Existing AI-based disaster mapping solutions often require extensive manual data annotation, limit generalisation across different disaster types, and rely on single-modal observations.
• RAPIDMap proposes a multi-agent pipeline designed for rapid, zero-shot, and interpretable disaster mapping.
• The framework integrates four distinct intelligent agents: Disaster Perception Agent (DPA), Image Restoration Agent (IRA), Damage Recognition Agent (DRA), and Disaster Mapping Agent (DMA).
• It combines remote sensing (satellite) and street-view data to eliminate the need for manual annotation.
• The system aims to provide reliable disaster mapping essential for effective emergency response and recovery efforts.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2609.00046