Mapping global methane emissions from space with deep learning

Google Research announces MAPL-EMIT, a deep-learning framework based on a vision transformer (Swin-S) for automated detection, enhancement, and source estimation of methane plumes from hyperspectral satellite data. The framework achieves 84% recall on expert-annotated plumes and is accompanied by a global plume database, trained model, synthetic plumes, and inference library released on Earth Engine, Kaggle, and GitHub.

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Methane Analysis and Plume Localization with EMIT (MAPL-EMIT) is a deep-learning framework that represents a significant step toward automating the detection, enhancement prediction, and source estimation of methane plumes globally.
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Mapping global methane emissions from space with deep learning

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