CrisiSense-RAG：面向快速灾害影响评估的多模态检索增强生成系统
CrisiSense-RAG: Crisis Sensing Multimodal Retrieval-Augmented Generation for Rapid Disaster Impact Assessment
AI总结：
本文提出CrisiSense-RAG系统，通过多模态检索增强生成技术，解决灾害影响评估中时空不对齐问题，实现零样本设置下的高精度灾害评估。
摘要：
及时且空间解析的灾害影响评估对有效应急响应至关重要。然而，自动化方法通常难以处理时间异步问题。实时人类报告捕捉到峰值灾害条件，而高分辨率卫星图像通常在峰值条件后获取，往往反映的是洪水退却而非最大范围。简单融合这些不一致的数据流可能导致危险的低估，当事件后图像覆盖已记录的峰值洪水时。我们提出了CrisiSense-RAG，这是一种多模态检索增强生成框架，将影响评估重新定义为在异构数据源上的证据综合，无需特定灾害的微调。系统采用混合密集-稀疏检索处理文本源，并使用CLIP基于检索处理航空图像。一个分管道架构输送至异步融合逻辑，优先考虑实时社会证据以确定峰值洪水范围，同时将图像视为结构破坏的持久证据。在飓风哈维的207个邮编查询上评估，该框架在零样本设置下实现了洪水范围MAE为10.94%至28.40%，损害严重性MAE为16.47%至21.65%。提示级别对齐对定量有效性至关重要，因为指标接地可将损害估计提高多达4.75个百分点。这些结果展示了在现实数据约束下快速恢复智能的实用且可部署的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
