Leveraging Multi-task Deep Learning for Quantitative Monitoring of Fugitive Methane Plumes from Spaceborne Hyperspectral Imagery

A deep learning framework is proposed for the spaceborne retrieval of methane emissions based on data simulation using Large Eddy Simulation (LES), Radiative Transfer Equation (RTE), and data augmentation techniques. The framework includes an instance segmentation algorithm for isolating and identifying methane emission sources, and multi-task learning models that outperform single-task models in methane concentration inversion, plume segmentation, and emission rate estimation.