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dc.contributor.author | Vaughan, Anna | es_ES |
dc.contributor.author | Mateo-García, Gonzalo | es_ES |
dc.contributor.author | Gómez-Chova, Luis | es_ES |
dc.contributor.author | Ruzicka, Vit | es_ES |
dc.contributor.author | Guanter-Palomar, Luis María | es_ES |
dc.contributor.author | Irakulis-Loitxate, Itziar | es_ES |
dc.date.accessioned | 2024-09-09T18:09:27Z | |
dc.date.available | 2024-09-09T18:09:27Z | |
dc.date.issued | 2024-05-03 | es_ES |
dc.identifier.issn | 1867-1381 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/207816 | |
dc.description.abstract | [EN] We present a deep learning model, CH4Net, for automated monitoring of methane super-emitters from Sentinel-2 data. When trained on images of 23 methane super-emitter locations from 2017-2020 and evaluated on images from 2021, this model detects 84 % of methane plumes compared with 24 % of plumes for a state-of-the-art baseline while maintaining a similar false positive rate. We present an in-depth analysis of CH4Net over the complete dataset and at each individual super-emitter site. In addition to the CH4Net model, we compile and make open source a hand-annotated training dataset consisting of 925 methane plume masks as a machine learning baseline to drive further research in this field. | es_ES |
dc.description.sponsorship | This research has been supported by the Spanish Ministry of Science, Innovation and Universities (project PID2019-109026RB-I00; funder ID:MCIN/AEI/10.13039/501100011033) and the European Social Fund. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | European Geosciences Union | es_ES |
dc.relation.ispartof | Atmospheric Measurement Techniques | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | CH4Net | es_ES |
dc.subject | Deep learning | es_ES |
dc.subject | Methane | es_ES |
dc.subject | Monitoring | es_ES |
dc.subject | Sentinel-2 imagery | es_ES |
dc.subject.classification | FISICA APLICADA | es_ES |
dc.title | CH4Net: a deep learning model for monitoring methane super-emitters with Sentinel-2 imagery | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.5194/amt-17-2583-2024 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109026RB-I00/ES/HERRAMIENTAS DE APRENDIZAJE PROFUNDO PARA LA DETECCION DE NUBES EN IMAGENES DE SATELITE DE OBSERVACION DE LA TIERRA/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Telecomunicación - Escola Tècnica Superior d'Enginyers de Telecomunicació | es_ES |
dc.description.bibliographicCitation | Vaughan, A.; Mateo-García, G.; Gómez-Chova, L.; Ruzicka, V.; Guanter-Palomar, LM.; Irakulis-Loitxate, I. (2024). CH4Net: a deep learning model for monitoring methane super-emitters with Sentinel-2 imagery. Atmospheric Measurement Techniques. 17(9):2583-2593. https://doi.org/10.5194/amt-17-2583-2024 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.5194/amt-17-2583-2024 | es_ES |
dc.description.upvformatpinicio | 2583 | es_ES |
dc.description.upvformatpfin | 2593 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 17 | es_ES |
dc.description.issue | 9 | es_ES |
dc.relation.pasarela | S\522950 | es_ES |
dc.contributor.funder | European Social Fund | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |