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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.09845 |
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| _version_ | 1866917004583108608 |
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| author | LaHaye, Nicholas Munashinge, Thilanka Lee, Hugo Pan, Xiaohua Abad, Gonzalo Gonzalez Mahmoud, Hazem Wei, Jennifer |
| author_facet | LaHaye, Nicholas Munashinge, Thilanka Lee, Hugo Pan, Xiaohua Abad, Gonzalo Gonzalez Mahmoud, Hazem Wei, Jennifer |
| contents | This work demonstrates the possibilities for improving wildfire and air quality management in the western United States by leveraging the unprecedented hourly data from NASA's TEMPO satellite mission and advances in self-supervised deep learning. Here we demonstrate the efficacy of deep learning for mapping the near real-time hourly spread of wildfire fronts and smoke plumes using an innovative self-supervised deep learning-system: successfully distinguishing smoke plumes from clouds using GOES-18 and TEMPO data, strong agreement across the smoke and fire masks generated from different sensing modalities as well as significant improvement over operational products for the same cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09845 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Harnessing Self-Supervised Deep Learning and Geostationary Remote Sensing for Advancing Wildfire and Associated Air Quality Monitoring: Improved Smoke and Fire Front Masking using GOES and TEMPO Radiance Data LaHaye, Nicholas Munashinge, Thilanka Lee, Hugo Pan, Xiaohua Abad, Gonzalo Gonzalez Mahmoud, Hazem Wei, Jennifer Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition This work demonstrates the possibilities for improving wildfire and air quality management in the western United States by leveraging the unprecedented hourly data from NASA's TEMPO satellite mission and advances in self-supervised deep learning. Here we demonstrate the efficacy of deep learning for mapping the near real-time hourly spread of wildfire fronts and smoke plumes using an innovative self-supervised deep learning-system: successfully distinguishing smoke plumes from clouds using GOES-18 and TEMPO data, strong agreement across the smoke and fire masks generated from different sensing modalities as well as significant improvement over operational products for the same cases. |
| title | Harnessing Self-Supervised Deep Learning and Geostationary Remote Sensing for Advancing Wildfire and Associated Air Quality Monitoring: Improved Smoke and Fire Front Masking using GOES and TEMPO Radiance Data |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.09845 |