# Machine Learning for Methane Detection and Quantification from Space - A survey

arXiv: https://arxiv.org/abs/2408.15122 · HTML: https://mlsanity.com/paper/2408.15122/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 151 references that are in the map.
- First posted on arXiv 27 Aug 2024.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [On the Foundations of Earth and Climate Foundation Models](https://mlsanity.com/paper/2405.04285/) (2024)
- [Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation](https://mlsanity.com/paper/2211.15429/) (2022)
- [Unlocking the Potential: Multi-task Deep Learning for Spaceborne Quantitative Monitoring of Fugitive Methane Plumes](https://mlsanity.com/paper/2401.12870/) (2024)
- [Autonomous Detection of Methane Emissions in Multispectral Satellite Data Using Deep Learning](https://mlsanity.com/paper/2308.11003/) (2023)

## Built on by (most cited first, 5 of 5)

- [HyperspectralViTs: General Hyperspectral Models for On-board Remote Sensing](https://mlsanity.com/paper/2410.17248/) (2024)
- [Optimizing Methane Detection On Board Satellites: Speed, Accuracy, and Low-Power Solutions for Resource-Constrained Hardware](https://mlsanity.com/paper/2507.01472/) (2025)
- [Operational machine learning for remote spectroscopic detection of CH4 point sources](https://mlsanity.com/paper/2511.07719/) (2025)
- [Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT](https://mlsanity.com/paper/2604.10094/) (2026)
- [A Fast Methane Detection Pipeline on Board Satellites Based on Mag1c-SAS and LinkNet](https://mlsanity.com/paper/2606.03675/) (2026)

## Abstract (arXiv)

Methane (CH4) is a potent anthropogenic greenhouse gas, contributing 86 times more to global warming than Carbon Dioxide (CO2) over 20 years, and it also acts as an air pollutant. Given its high radiative forcing potential and relatively short atmospheric lifetime (9±1 years), methane has important implications for climate change, therefore, cutting methane emissions is crucial for effective climate change mitigation. 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. The architecture and data used in such ML models will be discussed separately for methane plume segmentation and emission rate estimation. Traditionally, experts rely on labor-intensive manually adjusted methods for methane detection. However, ML approaches offer greater scalability. Our analysis reveals that ML models outperform traditional methods, particularly those based on convolutional neural networks (CNN), which are based on the U-net and transformer architectures. These ML models extract valuable information from methane-sensitive spectral data, enabling a more accurate detection. Challenges arise when comparing these methods due to variations in data, sensor specifications, and evaluation metrics. To address this, we discuss existing datasets and metrics, providing an overview of available resources and identifying open research problems. Finally, we explore potential future advances in ML, emphasizing approaches for model comparability, large dataset creation, and the European Union's forthcoming methane strategy.