Abstract
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.
Abstract (translated)
随着城市化和气候变化的加剧，城市热岛效应变得越来越频繁和严重。为了制定有效的缓解计划，城市需要详细的空气温度数据，然而，由于缺乏数据，传统的机器学习模型往往会产生不准确的预测，尤其是在服务不足的地区。基于全球非结构化数据训练的地空基础模型提供了一种有前景的替代方案，通过展示强大的泛化能力和仅需少量微调的特点来实现这一目标。 在这项研究中，通过对绿地冷却效应进行量化并将其与模型预测进行对比，以建立城市热模式的经验真实值，以此评估模型的准确性。随后对基础模型进行了微调，使其能够根据未来气候情景预测地表温度，并通过模拟修补任务展示了其实际价值，突显了它在缓解支持中的作用。 研究结果表明，基础模型为评价数据稀缺地区的城市热岛缓解策略提供了一种强大的方法，有助于建设更具气候适应性的城市。
URL
https://arxiv.org/abs/2510.18773
