Urban heat islands, defined as specific zones exhibiting substantially higher
temperatures than their immediate environs, pose significant threats to
environmental sustainability and public health. This study introduces a novel
machine-learning model that amalgamates data from the Sentinel-3 satellite,
meteorological predictions, and additional remote sensing inputs. The primary
aim is to generate detailed spatiotemporal maps that forecast the peak
temperatures within a 24-hour period in Turin. Experimental results validate
the model's proficiency in predicting temperature patterns, achieving a Mean
Absolute Error (MAE) of 2.09 degrees Celsius for the year 2023 at a resolution
of 20 meters per pixel, thereby enriching our knowledge of urban climatic
behavior. This investigation enhances the understanding of urban microclimates,
emphasizing the importance of cross-disciplinary data integration, and laying
the groundwork for informed policy-making aimed at alleviating the negative
impacts of extreme urban temperatures.

通过使用来自 Sentinel-3 卫星、气象预测和其他遥感输入的数据，本研究介绍了一种新颖的机器学习模型，旨在生成详细的时空地图，预测都灵市 24 小时内的最高温度。实验结果表明，该模型在预测温度模式方面表现出色，对于 2023 年分辨率为每像素 20 米的情况，均方根误差为 2.09 摄氏度，从而丰富了我们对城市气候行为的认识。这项研究增进了我们对城市微气候的理解，强调了跨学科数据整合的重要性，并为缓解极端城市温度的负面影响的知情决策制定奠定了基础。