Maximum Temperature Prediction Using Remote Sensing Data Via Convolutional Neural Network
<|reference_start|>Maximum Temperature Prediction Using Remote Sensing Data Via Convolutional Neural Network: 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.<|reference_end|>
@article{innocenti2024maximum,
    title={Maximum Temperature Prediction Using Remote Sensing Data Via
  Convolutional Neural Network},
    author={Lorenzo Innocenti, Giacomo Blanco, Luca Barco, Claudio Rossi},
    journal={arXiv preprint arXiv:2405.20731},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20731},
    primaryClass={cs.AI cs.LG}
}