CrisiSense-RAG: Crisis Sensing Multimodal Retrieval-Augmented Generation for Rapid Disaster Impact Assessment

TL;DR

CrisiSense-RAG is a multimodal framework that synthesizes heterogeneous data sources for rapid disaster impact assessment, effectively handling temporal asynchrony and providing accurate flood and damage estimates.

Contribution

The paper introduces CrisiSense-RAG, a novel retrieval-augmented generation system that fuses social reports and satellite imagery without disaster-specific fine-tuning.

Findings

Achieves flood extent MAE of 10.94% to 28.40% in zero-shot settings.

Damage severity MAE ranges from 16.47% to 21.65%.

Prompt-level alignment enhances damage estimate accuracy by up to 4.75 percentage points.

Abstract

Timely and spatially resolved disaster impact assessment is essential for effective emergency response. However, automated methods typically struggle with temporal asynchrony. Real-time human reports capture peak hazard conditions while high-resolution satellite imagery is frequently acquired after peak conditions. This often reflects flood recession rather than maximum extent. Naive fusion of these misaligned streams can yield dangerous underestimates when post-event imagery overrides documented peak flooding. We present CrisiSense-RAG, which is a multimodal retrieval-augmented generation framework that reframes impact assessment as evidence synthesis over heterogeneous data sources without disaster-specific fine-tuning. The system employs hybrid dense-sparse retrieval for text sources and CLIP-based retrieval for aerial imagery. A split-pipeline architecture feeds into asynchronous fusion logic that prioritizes real-time social evidence for peak flood extent while treating imagery as persistent evidence of structural damage. Evaluated on Hurricane Harvey across 207 ZIP-code queries, the framework achieves a flood extent MAE of 10.94% to 28.40% and damage severity MAE of 16.47% to 21.65% in zero-shot settings. Prompt-level alignment proves critical for quantitative validity because metric grounding improves damage estimates by up to 4.75 percentage points. These results demonstrate a practical and deployable approach to rapid resilience intelligence under real-world data constraints.