随着城市化进程和气候变化的推进，都市热岛效应日益频繁且严重。为制定有效的缓解策略，城市需要详细的气温数据，但传统机器学习模型在数据有限的情况下往往产生不准确的预测，尤其是在服务不足的区域。基于全球非结构化数据训练的地理空间基础模型提供了一种有前景的替代方案，其具备强大的泛化能力，仅需少量微调即可应用。本研究通过量化绿地的降温效应并将其与模型预测结果进行对比，建立了都市热模式的经验真实数据，用以评估模型的准确性。随后，对基础模型进行微调，以预测未来气候情景下的地表温度，并通过模拟修复（inpainting）展示了其在缓解支持中的实际价值。结果表明，基础模型为数据匮乏地区评估都市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。

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.