用微气候模型预测城市热岛，助力缺数据地区制定降温策略
Detection and Simulation of Urban Heat Islands Using a Fine-Tuned Geospatial Foundation Model for Microclimate Impact Prediction
- 用地理基础模型+少量调优，预测城市地表温度
- 验证绿地降温效果，模型在缺数据区仍准确
- 可模拟城市改造方案，适合城市规划与气候应对
随着城市化和气候变化加剧，城市热岛效应日益频繁且严重。为制定有效缓解措施，城市需要高精度的气温数据，但传统机器学习模型因数据有限，常在欠发达区域预测不准。本文利用全球非结构化数据训练的地理基础模型，具备强泛化能力，仅需少量微调即可应用。研究通过实证建立城市热模式基准，量化绿地降温效果，并与模型预测对比评估其准确性。进一步对模型进行微调，以预测未来气候情景下的地表温度，并通过模拟补全（inpainting）展示其在缓解策略支持中的实际价值。结果表明，该方法为数据稀缺地区评估热岛缓解策略提供了有力工具，有助于建设更具气候韧性的城市。
原文摘要 · Abstract (English)
As urbanization and climate change progress, urban heat island effects are becoming more frequent and severe. To formulate effective mitigation plans, cities require detailed air temperature data, yet conventional machine learning models with limited data often produce inaccurate predictions, particularly in underserved areas. Geospatial foundation models trained on global unstructured data offer a promising alternative by demonstrating strong generalization and requiring only minimal fine-tuning. In this study, an empirical ground truth of urban heat patterns is established by quantifying cooling effects from green spaces and benchmarking them against model predictions to evaluate the model's accuracy. The foundation model is subsequently fine-tuned to predict land surface temperatures under future climate scenarios, and its practical value is demonstrated through a simulated inpainting that highlights its role for mitigation support. The results indicate that foundation models offer a powerful way for evaluating urban heat island mitigation strategies in data-scarce regions to support more climate-resilient cities.
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