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

2026-08-30

AI Summary

为解决现有AI方法在灾害制图中需大量标注、缺乏跨灾种泛化能力及单模态依赖问题，提出RAPIDMap，利用多智能体处理卫星和街景图像以实现快速可靠的灾害地图绘制。

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.

Problem

• AI-based approaches
• manual annotation
• cross-hazard generalization
• single-modal observations

Innovation

• multi-agent pipeline
• zero-shot learning
• disaster mapping
• satellite and street-view imagery