Unlocking the Potential: Multi-task Deep Learning for Spaceborne Quantitative Monitoring of Fugitive Methane Plumes

• URL: http://arxiv.org/abs/2401.12870v3
• Date: Wed, 02 Oct 2024 14:32:19 GMT
• Title: Unlocking the Potential: Multi-task Deep Learning for Spaceborne Quantitative Monitoring of Fugitive Methane Plumes
• Authors: Guoxin Si, Shiliang Fu, Wei Yao,
• Abstract summary: Methane concentration inversion, plume segmentation, and emission rate estimation are three subtasks of methane emission monitoring. We introduce a novel deep learning-based framework for quantitative methane emission monitoring from remote sensing images. We train a U-Net network for methane concentration inversion, a Mask R-CNN network for methane plume segmentation, and a ResNet-50 network for methane emission rate estimation.
• Score: 0.7970333810038046
• License:
• Abstract: As global warming intensifies, increased attention is being paid to monitoring fugitive methane emissions and detecting gas plumes from landfills. We have divided methane emission monitoring into three subtasks: methane concentration inversion, plume segmentation, and emission rate estimation. Traditional algorithms face certain limitations: methane concentration inversion typically employs the matched filter, which is sensitive to the global spectrum distribution and prone to significant noise. There is scant research on plume segmentation, with many studies depending on manual segmentation, which can be subjective. The estimation of methane emission rate frequently uses the IME algorithm, which necessitates meteorological measurement data. Utilizing the WENT landfill site in Hong Kong along with PRISMA hyperspectral satellite imagery, we introduce a novel deep learning-based framework for quantitative methane emission monitoring from remote sensing images that is grounded in physical simulation. We create simulated methane plumes using large eddy simulation (LES) and various concentration maps of fugitive emissions using the radiative transfer equation (RTE), while applying augmentation techniques to construct a simulated PRISMA dataset. We train a U-Net network for methane concentration inversion, a Mask R-CNN network for methane plume segmentation, and a ResNet-50 network for methane emission rate estimation. All three deep networks yield higher validation accuracy compared to traditional algorithms. Furthermore, we combine the first two subtasks and the last two subtasks to design multi-task learning models, MTL-01 and MTL-02, both of which outperform single-task models in terms of accuracy. Our research exemplifies the application of multi-task deep learning to quantitative methane monitoring and can be generalized to a wide array of methane monitoring tasks.

Related papers

• Forecasting Smog Clouds With Deep Learning [6.144680854063938]
  We propose an integrated hierarchical model architecture inspired by air pollution dynamics and atmospheric science. Results demonstrate that, above all, the hierarchical GRU proves itself as a competitive and efficient method for forecasting the concentration of smog-related pollutants.
  arXiv  Detail & Related papers  (2024-10-03T17:59:13Z)
• 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)
• Modeling State Shifting via Local-Global Distillation for Event-Frame Gaze Tracking [61.44701715285463]
  This paper tackles the problem of passive gaze estimation using both event and frame data. We reformulate gaze estimation as the quantification of the state shifting from the current state to several prior registered anchor states. To improve the generalization ability, instead of learning a large gaze estimation network directly, we align a group of local experts with a student network.
  arXiv  Detail & Related papers  (2024-03-31T03:30:37Z)
• 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)
• 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)
• 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)
• MetricUNet: Synergistic Image- and Voxel-Level Learning for Precise CT Prostate Segmentation via Online Sampling [66.01558025094333]
  We propose a two-stage framework, with the first stage to quickly localize the prostate region and the second stage to precisely segment the prostate. We introduce a novel online metric learning module through voxel-wise sampling in the multi-task network. Our method can effectively learn more representative voxel-level features compared with the conventional learning methods with cross-entropy or Dice loss.
  arXiv  Detail & Related papers  (2020-05-15T10:37:02Z)
• Spatial and spectral deep attention fusion for multi-channel speech separation using deep embedding features [60.20150317299749]
  Multi-channel deep clustering (MDC) has acquired a good performance for speech separation. We propose a deep attention fusion method to dynamically control the weights of the spectral and spatial features and combine them deeply. Experimental results show that the proposed method outperforms MDC baseline and even better than the ideal binary mask (IBM)
  arXiv  Detail & Related papers  (2020-02-05T03:49:39Z)

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.