用微调的地理空间模型精准预测城市热岛，支持未来气候与绿化策略模拟。
Detection and Simulation of Urban Heat Islands Using a Fine-Tuned Geospatial Foundation Model
- 基于全球数据微调地理空间模型，实现像素级温度预测
- 预测误差低于1.74°C，外推能力达3.62°C
- 适合城市规划者评估气候变化与绿化方案影响
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。为制定有效缓解策略，城市需要高精度的气温数据。然而，传统机器学习方法受限于数据基础设施，常在欠发达地区预测不准。本文利用在非结构化全球数据上训练的地理空间基础模型，通过微调实现对城市地表温度的精准预测，并模拟不同植被策略下的响应。模型在像素级下采样中误差低于1.74 °C，与实测数据模式高度一致，具备高达3.62 °C的外推能力，显著提升复杂区域的预测可靠性。
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. However, predictive analytics methods based on conventional machine learning models and limited data infrastructure often provide inaccurate predictions, especially in underserved areas. In this context, geospatial foundation models trained on unstructured global data demonstrate strong generalization and require minimal fine-tuning, offering an alternative for predictions where traditional approaches are limited. This study fine-tunes a geospatial foundation model to predict urban land surface temperatures under future climate scenarios and explores its response to land cover changes using simulated vegetation strategies. The fine-tuned model achieved pixel-wise downscaling errors below 1.74 °C and aligned with ground truth patterns, demonstrating an extrapolation capacity up to 3.62 °C.
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