用大模型自动识别城市内涝并生成报告，提升应急响应效率
Automated urban waterlogging assessment and early warning through a mixture of foundation models
- 基于大模型的半监督微调与思维链提示，解决标注数据少的问题
- 在视觉基准上显著提升内涝识别性能，报告准确描述水深与风险等级
- 适合城市管理者、灾害应急团队和气候韧性研究者使用
随着气候变化加剧，城市内涝对全球公共安全和基础设施构成日益严重的威胁。现有监测手段严重依赖人工上报，难以实现及时全面的评估。本文提出基于基础模型的都市内涝评估框架UWAssess，可自动识别监控图像中的积水区域，并生成结构化评估报告。针对标注数据稀缺问题，设计了半监督微调策略与思维链（CoT）提示策略，充分释放基础模型在数据匮乏场景下的潜力。在多个具有挑战性的视觉基准测试中，感知性能显著提升。基于GPT的评估验证了UWAssess生成文本报告的可靠性，能准确描述内涝范围、深度、风险及影响。该双重能力推动内涝监测从感知向生成转变，多模型协同架构为智能、可扩展的城市管理系统奠定基础，支持城市治理、灾后响应与气候韧性建设。
原文摘要 · Abstract (English)
With climate change intensifying, urban waterlogging poses an increasingly severe threat to global public safety and infrastructure. However, existing monitoring approaches rely heavily on manual reporting and fail to provide timely and comprehensive assessments. In this study, we present Urban Waterlogging Assessment (UWAssess), a foundation model-driven framework that automatically identifies waterlogged areas in surveillance images and generates structured assessment reports. To address the scarcity of labeled data, we design a semi-supervised fine-tuning strategy and a chain-of-thought (CoT) prompting strategy to unleash the potential of the foundation model for data-scarce downstream tasks. Evaluations on challenging visual benchmarks demonstrate substantial improvements in perception performance. GPT-based evaluations confirm the ability of UWAssess to generate reliable textual reports that accurately describe waterlogging extent, depth, risk and impact. This dual capability enables a shift of waterlogging monitoring from perception to generation, while the collaborative framework of multiple foundation models lays the groundwork for intelligent and scalable systems, supporting urban management, disaster response and climate resilience.
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