# Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation

arXiv: https://arxiv.org/abs/2211.15429 · HTML: https://mlsanity.com/paper/2211.15429/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 17 Nov 2022.

## Builds on (most cited first, 2 of 2)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fast and Accurate Retrieval of Methane Concentration from Imaging Spectrometer Data Using Sparsity Prior](https://mlsanity.com/paper/2003.02978/) (2020)

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

- [Machine Learning for Methane Detection and Quantification from Space - A survey](https://mlsanity.com/paper/2408.15122/) (2024)
- [Operational machine learning for remote spectroscopic detection of CH4 point sources](https://mlsanity.com/paper/2511.07719/) (2025)
- [AttMetNet: Attention-Enhanced Deep Neural Network for Methane Plume Detection in Sentinel-2 Satellite Imagery](https://mlsanity.com/paper/2512.02751/) (2025)
- [Fully Automatic Trace Gas Plume Detection](https://mlsanity.com/paper/2605.03372/) (2026)
- [Robust Small Methane Plume Segmentation in Satellite Imagery](https://mlsanity.com/paper/2508.16282/) (2025)

## Abstract (arXiv)

The 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. To compensate for the relative scarcity of PRISMA images, we trained our model by transposing high resolution plumes from Sentinel-2 to PRISMA. Our methodology thus avoids computationally expensive synthetic plume generation from Large Eddy Simulations by generating a broad and realistic training database, and paves the way for large-scale detection of methane plumes using future hyperspectral sensors (EnMAP, EMIT, CarbonMapper).