RAPID: A Reproducible Multi-Agent Pipeline for Interpretable Disaster Damage Assessment from Satellite and Street-View Imagery

2026-06-19

AI Summary

Existing disaster loss assessment methods face limitations in cross-domain generalization, handling long-tailed distributions, and fusing multi-source heterogeneous geospatial data, hindering interpretable, zero-shot automated evaluation. This work proposes the first fine-tuning-free multi-agent collaborative framework that integrates satellite and street-view imagery to enable multi-hazard, multimodal joint reasoning. The framework supports zero-shot identification of disaster types, determination of damage severity levels, and generation of location-specific emergency recommendations. It incorporates modules for cross-view understanding, image inpainting, structured damage recognition, and geographic reasoning. Evaluated across hurricane, flood, wildfire, and earthquake scenarios, the approach achieves a multi-hazard classification accuracy of 0.92 and a peak cross-view damage prediction score of 0.627, demonstrating its potential as a foundational infrastructure for autonomous disaster intelligence.

Abstract

Due to the increasing frequency and intensity of extreme climate events, there is a clear demand for intelligent, scalable, and autonomous approaches to disaster damage assessment. Existing methods, largely based on supervised learning and task-specific fine-tuning, struggle to generalize under domain shifts, long-tailed data distributions, and heterogeneous geospatial data sources, especially in disaster scenarios. They also often lack the ability to integrate and reason across multimodal geospatial information, such as satellite images and street-view images. In this paper, we introduce RAPID, a reproducible multi-agent pipeline for interpretable disaster damage assessment, including damage-level assessment, damage-type interpretation, and actionable suggestions for response, remediation, and recovery. RAPID coordinates specialized agents to perform cross-view understanding, image restoration, structured damage recognition, and geographical reasoning across heterogeneous data modalities. Without task-specific fine-tuning, RAPID supports zero-shot damage assessment by jointly using complementary information from remote sensing and ground-level perspectives. The system produces fine-grained, interpretable assessments and automatically generates location-specific, decision-relevant disaster reports to support early-stage emergency response. We evaluate RAPID across hurricanes, floods, wildfires, and earthquakes using multiple cross-view imagery inputs, including pre- and post-disaster street-view images, post-disaster remote sensing imagery, and street-view image pairs. Experiments show that RAPID achieves 0.92 overall accuracy for multi-disaster type classification and up to 0.627 for cross-view damage severity prediction, highlighting its potential as a foundational framework for autonomous disaster intelligence.

Problem

• disaster damage assessment
• domain shift
• heterogeneous geospatial data
• multimodal reasoning
• generalization

Innovation

• multi-agent system
• zero-shot damage assessment
• cross-view geospatial reasoning
• interpretable disaster intelligence
• heterogeneous multimodal fusion