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

Autor:	A. Vaughan, G. Mateo-García, L. Gómez-Chova, V. Růžička, L. Guanter, I. Irakulis-Loitxate
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	Environmental engineering TA170-171 Earthwork. Foundations TA715-787
Zdroj:	Atmospheric Measurement Techniques, Vol 17, Pp 2583-2593 (2024)
Druh dokumentu:	article
ISSN:	1867-1381 1867-8548
DOI:	10.5194/amt-17-2583-2024
Popis:	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.
Databáze:	Directory of Open Access Journals
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