Maximum Temperature Prediction Using Remote Sensing Data Via Convolutional Neural Network

• URL: http://arxiv.org/abs/2405.20731v1
• Date: Fri, 31 May 2024 09:39:41 GMT
• Title: Maximum Temperature Prediction Using Remote Sensing Data Via Convolutional Neural Network
• Authors: Lorenzo Innocenti, Giacomo Blanco, Luca Barco, Claudio Rossi,
• Abstract summary: This study introduces a novel machine-learning model that amalgamates data from the Sentinel-3 satellite, meteorological predictions, and additional remote sensing inputs. 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.
• Score: 0.11249583407496218
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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