Maximum Temperature Prediction Using Remote Sensing Data Via Convolutional Neural Network
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916267472977920 |
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| author | Innocenti, Lorenzo Blanco, Giacomo Barco, Luca Rossi, Claudio |
| author_facet | Innocenti, Lorenzo Blanco, Giacomo Barco, Luca Rossi, Claudio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_20731 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Maximum Temperature Prediction Using Remote Sensing Data Via Convolutional Neural Network Innocenti, Lorenzo Blanco, Giacomo Barco, Luca Rossi, Claudio Artificial Intelligence Machine Learning I.2.10; G.3 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. |
| title | Maximum Temperature Prediction Using Remote Sensing Data Via Convolutional Neural Network |
| topic | Artificial Intelligence Machine Learning I.2.10; G.3 |
| url | https://arxiv.org/abs/2405.20731 |