RAPIDMap：用于从卫星和街景图像进行可解释灾害制图的快速多智能体流水线
RAPIDMap: Rapid Multi-Agent Pipeline for Interpretable Disaster Mapping from Satellite and Street-view Imagery
- Texas A&M University（德克萨斯农工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对现有AI灾害制图方法需大量人工标注等不足，提出RAPIDMap多智能体流水线，整合四类智能体并结合双模态数据，实现零样本跨灾害可解释制图，生成结构化情报及恢复建议。
AI中文摘要：
受影响区域、受损基础设施及受灾人口的快速可靠灾害制图对应急响应与恢复至关重要，但现有AI方法常需大量人工标注、缺乏跨灾害泛化能力且依赖单模态观测。为应对这些挑战，本文提出RAPIDMap——一种用于从卫星和街景图像进行零样本可解释灾害制图的快速多智能体流水线。该框架整合了四个智能体：灾害感知智能体（Disaster Perception Agent, DPA）、图像恢复智能体（Image Restoration Agent, IRA）、灾害识别智能体（Damage Recognition Agent, DRA）及灾害制图智能体（Disaster Mapping Agent, DMA）。通过结合遥感与街景数据，RAPIDMap无需人工微调，可跨多种灾害类别泛化，并生成结构化、可直接用于制图的灾害情报及恢复建议。
英文摘要：
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.
