用实时社交报告和卫星影像融合评估灾情，不需定制训练也能准确定位洪水范围和损毁程度。
CrisiSense-RAG: Crisis Sensing Multimodal Retrieval-Augmented Generation for Rapid Disaster Impact Assessment
- 结合社交平台实时报告与卫星图像，通过异步融合逻辑优先采信峰值灾情信息。
- 在飓风哈维测试中，洪水范围误差10.94%~28.40%，损毁严重度误差16.47%~21.65%。
- 无需微调即可零样本运行，适合应急响应、灾害监测等实战场景。
及时且空间精确的灾情评估对高效应急响应至关重要。然而，自动化方法常面临时间不同步问题：实时人类报告反映灾害峰值状态，而高分辨率卫星影像多在灾后获取，往往体现洪水退却而非最大范围。若简单融合这些错位数据，可能因灾后影像覆盖峰值记录而造成严重低估。本文提出CrisiSense-RAG，一种多模态检索增强生成框架，将灾情评估重构为异构数据源的证据合成，无需灾难特定微调。系统采用混合稠密-稀疏检索处理文本源，基于CLIP的检索匹配航拍图像。分路架构配合异步融合逻辑，优先采纳实时社交证据判断峰值洪水范围，将影像视为结构性损毁的持续证据。在飓风哈维207个邮编查询上评估，洪水范围平均绝对误差（MAE）为10.94%至28.40%，损毁严重度MAE为16.47%至21.65%，均处于零样本设置。提示层对齐对定量有效性至关重要，其提升使损毁估计准确率最高改善4.75个百分点。结果表明，该方法在真实数据约束下具备实用性和可部署性，适用于快速韧性智能分析。
原文摘要 · Abstract (English)
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.
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