Robust Small Methane Plume Segmentation in Satellite Imagery

• URL: http://arxiv.org/abs/2508.16282v1
• Date: Fri, 22 Aug 2025 10:41:50 GMT
• Title: Robust Small Methane Plume Segmentation in Satellite Imagery
• Authors: Khai Duc Minh Tran, Hoa Van Nguyen, Aimuni Binti Muhammad Rawi, Hareeshrao Athinarayanarao, Ba-Ngu Vo,
• Abstract summary: This paper tackles the challenging problem of detecting methane plumes, a potent greenhouse gas, using Sentinel-2 imagery.<n>We propose a novel deep learning solution based on U-Net with a ResNet34 encoder, integrating dual spectral enhancement techniques.<n> Experiments show our approach achieves a 78.39% F1-score on the validation set, demonstrating superior performance in sensitivity and precision.
• Score: 1.1200323437006519
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• AttMetNet: Attention-Enhanced Deep Neural Network for Methane Plume Detection in Sentinel-2 Satellite Imagery [1.0266286487433587]
  Methane is a powerful greenhouse gas that contributes significantly to global warming.<n>We present AttMetNet, a novel attention-enhanced deep learning framework for methane plume detection with Sentinel-2 satellite imagery.
  arXiv  Detail & Related papers  (2025-12-02T13:34:39Z)
• Optimizing Methane Detection On Board Satellites: Speed, Accuracy, and Low-Power Solutions for Resource-Constrained Hardware [0.0]
  Methane is a potent greenhouse gas, and detecting its leaks early via hyperspectral satellite imagery can help mitigate climate change.<n>Traditional methane enhancement methods are too computationally demanding for resource-limited onboard hardware.<n>This work accelerates methane detection by focusing on efficient, low-power algorithms.
  arXiv  Detail & Related papers  (2025-07-02T08:34:34Z)
• SNAT-YOLO: Efficient Cross-Layer Aggregation Network for Edge-Oriented Gangue Detection [1.7948767405202701]
  Our model achieves a detection accuracy of 99.10% in coal gangue detection tasks.<n>It reduces the model size by 38%,the number of parameters by 41%,and the computational cost by 40%,while decreasing the average detection time per image by 1 ms.
  arXiv  Detail & Related papers  (2025-02-09T18:39:35Z)
• 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)
• Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects [70.48554424894728]
  We develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depth-wise Self-Attention (DSA) module. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-22T06:19:11Z)
• 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)
• Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors [64.68349896377629]
  This work presents a novel autonomous end-to-end system that addresses the monocular relative localization, through deep neural networks (DNNs), of two peer nano-drones. To cope with the ultra-constrained nano-drone platform, we propose a vertically-integrated framework, including dataset augmentation, quantization, and system optimizations. Experimental results show that our DNN can precisely localize a 10cm-size target nano-drone by employing only low-resolution monochrome images, up to 2m distance.
  arXiv  Detail & Related papers  (2023-03-03T14:14:08Z)
• Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation [67.32835203947133]
  New generation of hyperspectral imagers, such as PRISMA, has improved significantly our detection capability of methane (CH4) plumes from space at high spatial resolution (30m) We present here a complete framework to identify CH4 plumes using images from the PRISMA satellite mission and a deep learning model able to detect plumes over large areas.
  arXiv  Detail & Related papers  (2022-11-17T17:36:05Z)
• Efficient Decoder-free Object Detection with Transformers [75.00499377197475]
  Vision transformers (ViTs) are changing the landscape of object detection approaches. We propose a decoder-free fully transformer-based (DFFT) object detector. DFFT_SMALL achieves high efficiency in both training and inference stages.
  arXiv  Detail & Related papers  (2022-06-14T13:22:19Z)

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.