Operational Deployment of Hyperspectral Machine Learning Models for Methane Leak Detection at the United Nations

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Vít Růžička, Gonzalo Mateo-García, Itziar Irakulis-Loitxate, Manuel Montesino San Martin, Juan Emmanuel Johnson, Anna Allen, Claudio Cifarelli, Luis Guanter

Published: 24 Dec 2025, Last Modified: 08 Jan 2026CrossrefEveryoneRevisionsCC BY-SA 4.0
Abstract: We present results from deploying machine learning models into production for detecting methane leaks in data from imaging spectroscopy satellites. We use and publicly release one of the largest global and most diverse datasets of methane plumes from three different imaging spectroscopy missions. We compute three different variants of methane enhancement products and quantitatively compare their suitability for using with deep learning models (showing an improvement of more than 47% in the F1 score). Using the data from different sensors, we demonstrate generalisation capabilities and transfer knowledge learned on the largest dataset to the other smaller sets. With attention to the capabilities needed for model deployment, we extend prior evaluation methodologies from small tiled datasets to full granule evaluation. Using these, we identify that most typically used methods create a large number of false alerts. We propose model ensembles, which reduce the number of false alarms by over 57%, while further improving the F1 score by additional 3.2% over the non ensembled variant. Finally, we deploy the proposed models in a real methane leak detection pipeline used at the United Nations Environment Programme’s Methane Alert and Response System. Detections of our model are used as proposals for a team of analysts, which effectively speeds up the detection process and reduces the time needed to make these detections on each capture. In 9 months our system helped detect over 1465 methane leak events in data from EMIT, PRISMA and EnMAP. The created datasets are part of a living system and ready to be used for model adaptation and improvement over time. Our work is an important step towards a global AI assisted methane leak detection system, which will be necessary in the light of a large number of new imaging spectroscopy missions being launched by NASA, ESA and private actors. Relying solely on manual data detection is no longer feasible.