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

Abstract

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.