[2511.07719] Operational machine learning for remote spectroscopic detection of CH$_{4}$ point sources

Computer Science > Artificial Intelligence arXiv:2511.07719 (cs) [Submitted on 11 Nov 2025 (v1), last revised 24 Mar 2026 (this version, v2)] Title:Operational machine learning for remote spectroscopic detection of CH$_{4}$ point sources Authors:Vít Růžička, Gonzalo Mateo-García, Itziar Irakulis-Loitxate, Juan Emmanuel Johnson, Manuel Montesino San Martín, Anna Allen, Alma Raunak, Carol Castaneda, Luis Guanter, David R. Thompson View a PDF of the paper titled Operational machine learning for remote spectroscopic detection of CH$_{4}$ point sources, by V\'it R\r{u}\v{z}i\v{c}ka and 9 other authors View PDF HTML (experimental) Abstract:Mitigating anthropogenic methane sources is one of the most cost-effective levers to slow down global warming. While satellite-based imaging spectrometers, such as EMIT, PRISMA, and EnMAP, can detect these point sources, current methane retrieval methods based on matched filters produce a high number of false detections requiring manual verification. To address this challenge, we deployed a ML system for detecting methane emissions within the Methane Alert and Response System (MARS) of UNEP's IMEO. This represents the first operational deployment of automated methane point-source detection using spaceborne imaging spectrometers, providing regular global coverage and scalability to future constellations with even higher data volumes. This task required several technical advances. First, we created one of the largest and most diverse and global ...