Robust Small Methane Plume Segmentation in Satellite Imagery
Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Tran, Khai Duc Minh, Van Nguyen, Hoa, Rawi, Aimuni Binti Muhammad, Athinarayanarao, Hareeshrao, Vo, Ba-Ngu
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W7077482147
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