{"ID":2879403,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.16282","arxiv_id":"2508.16282","title":"Robust Small Methane Plume Segmentation in Satellite Imagery","abstract":"This paper tackles the challenging problem of detecting methane plumes, a potent greenhouse gas, using Sentinel-2 imagery. This contributes to the mitigation of rapid climate change. We propose a novel deep learning solution based on U-Net with a ResNet34 encoder, integrating dual spectral enhancement techniques (Varon ratio and Sanchez regression) to optimise input features for heightened sensitivity. A key achievement is the ability to detect small plumes down to 400 m2 (i.e., for a single pixel at 20 m resolution), surpassing traditional methods limited to larger plumes. Experiments show our approach achieves a 78.39% F1-score on the validation set, demonstrating superior performance in sensitivity and precision over existing remote sensing techniques for automated methane monitoring, especially for small plumes.","short_abstract":"This paper tackles the challenging problem of detecting methane plumes, a potent greenhouse gas, using Sentinel-2 imagery. This contributes to the mitigation of rapid climate change. We propose a novel deep learning solution based on U-Net with a ResNet34 encoder, integrating dual spectral enhancement techniques (Varon...","url_abs":"https://arxiv.org/abs/2508.16282","url_pdf":"https://arxiv.org/pdf/2508.16282v1","authors":"[\"Khai Duc Minh Tran\",\"Hoa Van Nguyen\",\"Aimuni Binti Muhammad Rawi\",\"Hareeshrao Athinarayanarao\",\"Ba-Ngu Vo\"]","published":"2025-08-22T10:41:50Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"eess.SP\"]","methods":"[]","has_code":false}