| Popis: |
We present a deep learning model, CH4Net, for automated monitoring of methane super-emitters from Sentinel-2 data. When trained on images of 21 methane super-emitters from 2017–2020 and evaluated on images from 2021 this model achieves a scene-level accuracy of 0.83 and pixel-level balanced accuracy of 0.77. For individual emitters, accuracy is greater than 0.8 for 17 out of the 21 sites. We further demonstrate that CH4Net can successfully be applied to monitor two superemitter locations with similar background characteristics not included in the training set, with accuracies of 0.92 and 0.96. In addition to the CH4Net model we compile and open source a hand annotated training dataset consisting of 925 methane plume masks. |
