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

arXiv: https://arxiv.org/abs/2609.00046 · HTML: https://mlsanity.com/paper/2609.00046/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 30 Aug 2026.

## Builds on (most cited first, 8 of 8)

- [BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response](https://mlsanity.com/paper/2501.06019/) (2025)
- [Detecting natural disasters, damage, and incidents in the wild](https://mlsanity.com/paper/2008.09188/) (2020)
- [Cross-View Geolocalization and Disaster Mapping with Street-View and VHR Satellite Imagery: A Case Study of Hurricane IAN](https://mlsanity.com/paper/2408.06761/) (2024)
- [Harnessing Large Language Models for Disaster Management: A Survey](https://mlsanity.com/paper/2501.06932/) (2025)
- [Generalizable Disaster Damage Assessment via Change Detection with Vision Foundation Model](https://mlsanity.com/paper/2406.08020/) (2024)
- [Hyperlocal disaster damage assessment using bi-temporal street-view imagery and pre-trained vision models](https://mlsanity.com/paper/2504.09066/) (2025)
- [Integration of Large Vision Language Models for Efficient Post-disaster Damage Assessment and Reporting](https://mlsanity.com/paper/2411.01511/) (2024)
- [A Multimodal, Multilingual, and Multidimensional Pipeline for Fine-grained Crowdsourcing Earthquake Damage Evaluation](https://mlsanity.com/paper/2506.03360/) (2025)

## Abstract (arXiv)

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.