Artificial intelligence for methane detection: from continuous monitoring to verified mitigation

• URL: http://arxiv.org/abs/2511.21777v1
• Date: Wed, 26 Nov 2025 08:16:10 GMT
• Title: Artificial intelligence for methane detection: from continuous monitoring to verified mitigation
• Authors: Anna Allen, Gonzalo Mateo-Garcia, Itziar Irakulis-Loitxate, Manuel Montesino-San Martin, Marc Watine, James Requeima, Javier Gorroño, Cynthia Randles, Tharwat Mokalled, Luis Guanter, Richard E. Turner, Claudio Cifarelli, Manfredi Caltagirone,
• Abstract summary: Methane is a potent greenhouse gas, responsible for roughly 30% of warming since pre-industrial times.<n>Here, we introduce MARS-S2L, a machine learning model that detects methane emissions in publicly available multispectral satellite imagery.
• Score: 13.281959707326992
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 1,015 notifications to stakeholders in 20 countries, enabling verified, permanent mitigation of six persistent emitters, including a previously unknown site in Libya. These results demonstrate a scalable pathway from satellite detection to quantifiable methane mitigation.

Related papers

• Operational machine learning for remote spectroscopic detection of CH$_{4}$ point sources [1.0934673357973288]
  Mitigating anthropogenic methane sources is one of the most cost-effective levers to slow down global warming.<n>Current methane retrieval methods based on matched filters still produce a high number of false detections.<n>This paper describes the operational deployment of a machine learning system for detecting methane emissions.
  arXiv  Detail & Related papers  (2025-11-11T00:53:03Z)
• Don't Deceive Me: Mitigating Gaslighting through Attention Reallocation in LMMs [102.18178065928426]
  GasEraser is a training-free approach that reallocates attention weights from misleading textual tokens to semantically salient visual regions.<n>GasEraser significantly improves LMM robustness without requiring retraining or additional supervision.<n>For LLaVA-v1.5-7B, GasEraser reduces the misguidance rate by 48.2%, demonstrating its potential for more trustworthy LMMs.
  arXiv  Detail & Related papers  (2025-04-13T06:47:32Z)
• Machine Learning for Methane Detection and Quantification from Space -- A survey [49.7996292123687]
  Methane (CH_4) is a potent anthropogenic greenhouse gas, contributing 86 times more to global warming than Carbon Dioxide (CO_2) over 20 years. This work expands existing information on operational methane point source detection sensors in the Short-Wave Infrared (SWIR) bands. It reviews the state-of-the-art for traditional as well as Machine Learning (ML) approaches.
  arXiv  Detail & Related papers  (2024-08-27T15:03:20Z)
• AI for operational methane emitter monitoring from space [14.274401014063018]
  Mitigating methane emissions is the fastest way to stop global warming in the short-term. We present MARS-S2L, an automated AI-driven methane emitter monitoring system for Sentinel-2 and Landsat satellite imagery. Running this system operationally for six months has yielded 457 near-real-time detections in 22 different countries.
  arXiv  Detail & Related papers  (2024-08-08T20:06:37Z)
• A Comprehensive Survey of Contamination Detection Methods in Large Language Models [68.10605098856087]
  With the rise of Large Language Models (LLMs) in recent years, abundant new opportunities are emerging, but also new challenges.<n>LLMs' performance may not be reliable anymore, as the high performance may be at least partly due to their previous exposure to the data.<n>This limitation jeopardizes real capability improvement in the field of NLP, yet there remains a lack of methods on how to efficiently detect contamination.
  arXiv  Detail & Related papers  (2024-03-31T14:32:02Z)
• Autonomous Detection of Methane Emissions in Multispectral Satellite Data Using Deep Learning [73.01013149014865]
  Methane is one of the most potent greenhouse gases. Current methane emission monitoring techniques rely on approximate emission factors or self-reporting. Deep learning methods can be leveraged to automatize the detection of methane leaks in Sentinel-2 satellite multispectral data.
  arXiv  Detail & Related papers  (2023-08-21T19:36:50Z)
• MethaneMapper: Spectral Absorption aware Hyperspectral Transformer for Methane Detection [13.247385727508155]
  Methane is the chief contributor to global climate change. We propose a novel end-to-end spectral absorption wavelength aware transformer network, MethaneMapper, to detect and quantify the emissions. MethaneMapper achieves 0.63 mAP in detection and reduces the model size (by 5x) compared to the current state of the art.
  arXiv  Detail & Related papers  (2023-04-05T22:15:18Z)
• METER-ML: A Multi-sensor Earth Observation Benchmark for Automated Methane Source Mapping [2.814379852040968]
  Deep learning can identify the locations and characteristics of methane sources. There is a substantial lack of publicly available data to enable machine learning researchers and practitioners to build automated mapping approaches. We construct a multi-sensor dataset called METER-ML containing 86,625 georeferenced NAIP, Sentinel-1, and Sentinel-2 images in the U.S. We find that our best model achieves an area under the precision recall curve of 0.915 for identifying concentrated animal feeding operations and 0.821 for oil refineries and petroleum terminals on an expert-labeled test set.
  arXiv  Detail & Related papers  (2022-07-22T16:12:07Z)
• A Multi-Stage model based on YOLOv3 for defect detection in PV panels based on IR and Visible Imaging by Unmanned Aerial Vehicle [65.99880594435643]
  We propose a novel model to detect panel defects on aerial images captured by unmanned aerial vehicle. The model combines detections of panels and defects to refine its accuracy. The proposed model has been validated on two big PV plants in the south of Italy.
  arXiv  Detail & Related papers  (2021-11-23T08:04:32Z)
• Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery [59.32805936205217]
  Cattle farming is responsible for 8.8% of greenhouse gas emissions worldwide. We obtained satellite imagery of the Amazon at 40cm resolution, and compiled a dataset of 903 images containing a total of 28498 cattle. Our experiments show promising results and highlight important directions for the next steps on both counting algorithms and the data collection process for solving such challenges.
  arXiv  Detail & Related papers  (2020-11-14T19:07:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.