  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.
