Articles | Volume 17, issue 9
https://doi.org/10.5194/amt-17-2583-2024
https://doi.org/10.5194/amt-17-2583-2024
Research article
 | 
03 May 2024
Research article |  | 03 May 2024

CH4Net: a deep learning model for monitoring methane super-emitters with Sentinel-2 imagery

Anna Vaughan, Gonzalo Mateo-García, Luis Gómez-Chova, Vít Růžička, Luis Guanter, and Itziar Irakulis-Loitxate

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Subject: Gases | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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Cited articles

Aybar, C., Ysuhuaylas, L., Loja, J., Gonzales, K., Herrera, F., Bautista, L., Yali, R., Flores, A., Diaz, L., Cuenca, N., Espinoza, W., Prudencio, F., Llactayo, V., Montero, D., Sudmanns, M., Tiede, D., Mateo-García, G., and Gómez-Chova, L.: CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2, Scientific Data, 9, 782, https://doi.org/10.1038/s41597-022-01878-2, 2022. a
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale, arXiv [preprint], https://doi.org/10.48550/arXiv.2010.11929, 3 June 2020. a
Ehret, T., De Truchis, A., Mazzolini, M., Morel, J.-M., D’aspremont, A., Lauvaux, T., Duren, R., Cusworth, D., and Facciolo, G.: Global tracking and quantification of oil and gas methane emissions from recurrent sentinel-2 imagery, Environ. Sci. Technol., 56, 10517–10529, 2022. a, b, c
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Short summary
Methane is a potent greenhouse gas that has been responsible for around 25 % of global warming since the industrial revolution. Consequently identifying and mitigating methane emissions comprise an important step in combating the climate crisis. We develop a new deep learning model to automatically detect methane plumes from satellite images and demonstrate that this can be applied to monitor large methane emissions resulting from the oil and gas industry.
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