UHI-Bench：针对不同气候区城市的双源城市热岛建模基准测试
UHI-Bench: Benchmarking Dual-Source Urban Heat Island Modeling Across Cities in Diverse Climate Regimes
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- Technical University of Munich（慕尼黑工业大学）
- Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院）
- RWTH Aachen University（亚琛工业大学）
- Hainan Bielefeld University of Applied Sciences（海南比勒费尔德应用科学大学）
- Heilbronn Data Science Center（海尔布隆数据科学中心）
- Munich Data Science Institute（慕尼黑数据科学研究所）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
本研究推出首个双源城市热岛建模基准UHI-Bench，评估多模型在多城市多气候区的表现，为城市热建模提供指导并支持气候研究发展。
中文摘要 AI 辅助
城市热岛（UHI）在气候变化下不断加剧，加剧了热暴露风险。其两种主要观测类型：地表温度城市热岛（LST-UHI）和近地表气温城市热岛（AirT-UHI），捕捉了城市热的物理上不同的方面。然而，大多数研究依赖单一来源，用其中一个替代另一个会大幅偏倚人类热暴露的量级和空间变异性。准确的UHI建模还需要动态气象驱动因素和静态城市形态特征，但时空不兼容性阻碍了两者的对齐。LST观测中的云隙和稀疏的AirT站点网络进一步限制了双源UHI建模，推动了不同气候下的跨城市迁移。为弥合这些差距，我们推出UHI-Bench，这是首个用于双源UHI建模的基准，整合了动态和静态环境上下文。遵循统一的信号、机制和迁移框架，它在20个城市、9种柯本气候分类下，评估了四个模型家族的20多个基线模型在五项任务上的表现。结果显示，没有模型始终表现最佳，尽管基础模型始终具有竞争力且稳定。环境协变量通常能提升性能，但其效用因来源和任务而异。跨城市可迁移性更好地由UHI regime的重叠而非气候区相似性解释。凭借该数据集和标准化流程，我们的工作为城市热建模提供了实用指导，促进了气候数据公平，并支持气候研究的未来进展。
英文摘要
Urban heat islands (UHIs) are intensifying under climate change, exacerbating thermal exposure risks. Their two primary observations, land surface temperature UHI (LST-UHI) and near-surface air temperature UHI (AirT-UHI), capture physically distinct aspects of urban heat. However, most studies rely on a single source, and substituting one for the other can substantially bias the magnitude and spatial variability of human heat exposure. Accurate UHI modeling also requires dynamic meteorological drivers and static urban morphology features, but spatiotemporal incompatibilities hinder their alignment. Cloud gaps in LST observations and sparse AirT station networks further limit dual-source UHI modeling, motivating cross-city transfer across diverse climates. To bridge these gaps, we introduce UHI-Bench, the first UHI benchmark for dual-source UHI modeling that integrates dynamic and static environmental context. Following a unified signal, mechanism, and transfer framework, it evaluates over 20 baselines from four model families on five tasks across 20 cities and nine Köppen climate classes. Results show that no model is uniformly best, although foundation models remain consistently competitive and stable. Environmental covariates generally improve performance, but their utility varies across sources and tasks. Cross-city transferability is better explained by overlap in UHI regimes than by climate-zone similarity. With the dataset and standardized pipeline, our work provides practical guidance for urban heat modeling, promotes climate data equity, and supports future advances in climate research.
