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

arXiv: https://arxiv.org/abs/2401.12870 · HTML: https://mlsanity.com/paper/2401.12870/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 64 references that are in the map.
- First posted on arXiv 23 Jan 2024.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [End-to-End Multi-Task Learning with Attention](https://mlsanity.com/paper/1803.10704/) (2018)
- [On the Expressive Power of Deep Neural Networks](https://mlsanity.com/paper/1606.05336/) (2016)
- [Exploring Relational Context for Multi-Task Dense Prediction](https://mlsanity.com/paper/2104.13874/) (2021)
- [HySpecNet-11k: A Large-Scale Hyperspectral Dataset for Benchmarking Learning-Based Hyperspectral Image Compression Methods](https://mlsanity.com/paper/2306.00385/) (2023)
- [Hashing as Tie-Aware Learning to Rank](https://mlsanity.com/paper/1705.08562/) (2017)
- [Fast and Accurate Retrieval of Methane Concentration from Imaging Spectrometer Data Using Sparsity Prior](https://mlsanity.com/paper/2003.02978/) (2020)
- [Instance segmentation of fallen trees in aerial color infrared imagery using active multi-contour evolution with fully convolutional network-based intensity priors](https://mlsanity.com/paper/2105.01998/) (2021)
- [Impact of Scene-Specific Enhancement Spectra on Matched Filter Greenhouse Gas Retrievals from Imaging Spectroscopy](https://mlsanity.com/paper/2107.05578/) (2021)

## Built on by (most cited first, 3 of 3)

- [Machine Learning for Methane Detection and Quantification from Space - A survey](https://mlsanity.com/paper/2408.15122/) (2024)
- [AttMetNet: Attention-Enhanced Deep Neural Network for Methane Plume Detection in Sentinel-2 Satellite Imagery](https://mlsanity.com/paper/2512.02751/) (2025)
- [Plume Segmentation from MethaneSAT with Cross-Sensor Transfer Learning and Physics-Informed Postprocessing](https://mlsanity.com/paper/2605.24273/) (2026)

## Abstract (arXiv)

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.