Mapping global methane emissions from space with deep learning
Google Research 1 month ago 3
MAPL-EMIT, a deep-learning framework, was published to automate methane plume detection and localization from NASA’s EMIT hyperspectral satellite data using a vision transformer and physics-based training. It was trained on 3.6 million synthetic methane plumes inserted into real EMIT scenes and captured 84% of expert-annotated plumes on the gold-standard L2B dataset. The result is a higher-throughput mapping workflow plus released resources—global plume data on Earth Engine, datasets and a trained model on Kaggle, and an inference library on GitHub—to enable faster emissions mitigation actions.