{
  "abstract": "Automated detection and masking of individual methane plumes from satellite imagery is important for operational emission attribution and quantification. We present a machine learning framework for plume detection from MethaneSAT retrieved column-averaged dry-air mole fractions of methane. We address two core challenges: the scarcity of labeled MethaneSAT data and the need for inference reliability across diverse atmospheric and surface conditions. We first demonstrate that Mask R-CNN with a ResNet-50 backbone outperforms a U-Net semantic segmentation baseline on both MethaneAIR (an airborne version of MethaneSAT) and MethaneSAT data, with pixel-level F1 score gains of 10.49 and 5.48 percentage points respectively. To address MethaneSAT data scarcity, we evaluate three cross-sensor transfer strategies leveraging MethaneAIR flights and synthetic plumes. Mask R-CNN with ResNet-50 fine-tuned from MethaneAIR pre-trained weights is the most effective strategy, achieving instance-level precision of 0.60 and recall of 0.98 at the baseline operating point. A physics-informed post-processing pipeline converts detections into two operationally distinct modes. The first is a high-sensitivity mode that applies morphological filtering and proximity-based merging for comprehensive emission screening, achieving precision of 0.71 and recall of 0.94. The second is a high-precision mode that additionally applies a distribution-based classifier for confident source attribution, achieving precision of 0.92 and recall of 0.70. Manual review of detections classified as false positives against our wavelet-based reference plume mask labels shows that some correspond to real methane enhancements... Our data and code used in this work are publicly available at: https://doi.org/10.7910/DVN/FR959H",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.24273",
  "author_total": 19,
  "authors": [
    {
      "name": "Manuel Pérez-Carrasco"
    },
    {
      "name": "Maya Nasr"
    },
    {
      "name": "Zhan Zhang"
    },
    {
      "name": "Apisada Chulakadabba"
    },
    {
      "name": "Javier Roger"
    },
    {
      "name": "Raia Ottenheimer"
    },
    {
      "name": "Sébastien Roche"
    },
    {
      "name": "Maryann Sargent"
    },
    {
      "name": "Chris Chan Miller"
    },
    {
      "name": "Daniel Varon"
    },
    {
      "name": "Jack Warren"
    },
    {
      "name": "Luis Guanter"
    },
    {
      "name": "Kang Sun"
    },
    {
      "name": "Jonathan Franklin"
    },
    {
      "name": "Jia Chen"
    },
    {
      "name": "Cecilia Garraffo"
    },
    {
      "name": "Xiong Liu"
    },
    {
      "name": "Ritesh Gautam"
    },
    {
      "name": "Steven Wofsy"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dq8rnt4f6gr22.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.24273",
  "fulltext_url": null,
  "id": "dq8rnt4f6gr22",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.24273",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CV"
    ],
    [
      "arxiv",
      "physics.ao-ph"
    ],
    [
      "openalex",
      "2306|Atmospheric and Environmental Gas Dynamics"
    ]
  ],
  "openalex": "W7162444137",
  "pdf_url": "https://arxiv.org/pdf/2605.24273",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dq8rnt4f6gr22.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:06Z",
      "native_id": "2605.24273",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.24273"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.ao-ph",
      "subfield": "atmospheric"
    }
  ],
  "title": "Plume Segmentation from MethaneSAT with Cross-Sensor Transfer Learning and Physics-Informed Postprocessing",
  "title_text": "Plume Segmentation from MethaneSAT with Cross-Sensor Transfer Learning and Physics-Informed Postprocessing",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:06Z",
  "url": "https://research.draconitic.com/paper/dq8rnt4f6gr22",
  "venue": "arXiv",
  "versions": []
}