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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2511.21777 |
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| _version_ | 1866913058705637376 |
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| author | Mateo-Garcia, Gonzalo Allen, Anna Irakulis-Loitxate, Itziar Martin, Manuel Montesino-San Watine, Marc Randles, Cynthia Mokalled, Tharwat Raunak, Alma Castañeda-Martinez, Carol Jonhson, Juan E. Gorroño, Javier Requeima, James Cifarelli, Claudio Guanter, Luis Turner, Richard E. Caltagirone, Manfredi |
| author_facet | Mateo-Garcia, Gonzalo Allen, Anna Irakulis-Loitxate, Itziar Martin, Manuel Montesino-San Watine, Marc Randles, Cynthia Mokalled, Tharwat Raunak, Alma Castañeda-Martinez, Carol Jonhson, Juan E. Gorroño, Javier Requeima, James Cifarelli, Claudio Guanter, Luis Turner, Richard E. Caltagirone, Manfredi |
| contents | Methane is a potent greenhouse gas, responsible for roughly 30% of warming since pre-industrial times. A small number of large point sources account for a disproportionate share of emissions, creating an opportunity for substantial reductions by targeting relatively few sites. Detection and attribution of large emissions at scale for notification to asset owners remains challenging. Here, we introduce MARS-S2L, a machine learning model that detects methane emissions in publicly available multispectral satellite imagery. Trained on a manually curated dataset of over 80,000 images, the model provides high-resolution detections every two days, enabling facility-level attribution and identifying 78% of plumes with an 8% false positive rate at 697 previously unseen sites. Deployed operationally, MARS-S2L has issued 2,776 notifications to stakeholders in 25 countries, enabling verified, permanent mitigation of six persistent emitters, including a super-emitter in Algeria that had been releasing approximately 27,000 tonnes of methane annually for at least a decade and a previously unknown emitter in Libya first identified by MARS-S2L. These results demonstrate a scalable pathway from satellite detection to quantifiable methane mitigation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21777 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Artificial intelligence for methane detection: from continuous monitoring to verified mitigation Mateo-Garcia, Gonzalo Allen, Anna Irakulis-Loitxate, Itziar Martin, Manuel Montesino-San Watine, Marc Randles, Cynthia Mokalled, Tharwat Raunak, Alma Castañeda-Martinez, Carol Jonhson, Juan E. Gorroño, Javier Requeima, James Cifarelli, Claudio Guanter, Luis Turner, Richard E. Caltagirone, Manfredi Machine Learning Methane is a potent greenhouse gas, responsible for roughly 30% of warming since pre-industrial times. A small number of large point sources account for a disproportionate share of emissions, creating an opportunity for substantial reductions by targeting relatively few sites. Detection and attribution of large emissions at scale for notification to asset owners remains challenging. Here, we introduce MARS-S2L, a machine learning model that detects methane emissions in publicly available multispectral satellite imagery. Trained on a manually curated dataset of over 80,000 images, the model provides high-resolution detections every two days, enabling facility-level attribution and identifying 78% of plumes with an 8% false positive rate at 697 previously unseen sites. Deployed operationally, MARS-S2L has issued 2,776 notifications to stakeholders in 25 countries, enabling verified, permanent mitigation of six persistent emitters, including a super-emitter in Algeria that had been releasing approximately 27,000 tonnes of methane annually for at least a decade and a previously unknown emitter in Libya first identified by MARS-S2L. These results demonstrate a scalable pathway from satellite detection to quantifiable methane mitigation. |
| title | Artificial intelligence for methane detection: from continuous monitoring to verified mitigation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.21777 |