# Optimizing Methane Detection On Board Satellites: Speed, Accuracy, and Low-Power Solutions for Resource-Constrained Hardware

arXiv: https://arxiv.org/abs/2507.01472 · HTML: https://mlsanity.com/paper/2507.01472/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://mlsanity.com/paper/1707.03718/) (2017)
- [Stateful Dataflow Multigraphs: A Data-Centric Model for Performance Portability on Heterogeneous Architectures](https://mlsanity.com/paper/1902.10345/) (2019)
- [Fast and Accurate Retrieval of Methane Concentration from Imaging Spectrometer Data Using Sparsity Prior](https://mlsanity.com/paper/2003.02978/) (2020)
- [Fast model inference and training on-board of Satellites](https://mlsanity.com/paper/2307.08700/) (2023)
- [HyperspectralViTs: General Hyperspectral Models for On-board Remote Sensing](https://mlsanity.com/paper/2410.17248/) (2024)
- [Machine Learning for Methane Detection and Quantification from Space - A survey](https://mlsanity.com/paper/2408.15122/) (2024)

## Built on by (most cited first, 4 of 4)

- [FLAME: Physics-Guided Neural Operators for Onboard Satellite Methane Detection in Hyperspectral Imagery](https://mlsanity.com/paper/2606.01577/) (2026)
- [A Fast Methane Detection Pipeline on Board Satellites Based on Mag1c-SAS and LinkNet](https://mlsanity.com/paper/2606.03675/) (2026)
- [On-board ML for Trace Gas detection in Imaging Spectroscopy data](https://mlsanity.com/paper/2609.04458/) (2026)
- [Methane Detection On Board Satellites from Unorthorectified Imagery](https://mlsanity.com/paper/2609.04906/) (2026)

## Abstract (arXiv)

Methane is a potent greenhouse gas, and detecting its leaks early via hyperspectral satellite imagery can help mitigate climate change. Meanwhile, many existing missions operate in manual tasking regimes only, thus missing potential events of interest. To overcome slow downlink rates cost-effectively, onboard detection is a viable solution. However, traditional methane enhancement methods are too computationally demanding for resource-limited onboard hardware. This work accelerates methane detection by focusing on efficient, low-power algorithms. We test fast target detection methods (ACE, CEM) that have not been previously used for methane detection and propose a Mag1c-SAS - a significantly faster variant of the current state-of-the-art algorithm for methane detection: Mag1c. To explore their true detection potential, we integrate them with a machine learning model (U-Net, LinkNet). Our results identify two promising candidates (Mag1c-SAS and CEM), both acceptably accurate for the detection of strong plumes and computationally efficient enough for onboard deployment: one optimized more for accuracy, the other more for speed, achieving up to ~100x and ~230x faster computation than original Mag1c on resource-limited hardware. Additionally, we propose and evaluate three band selection strategies. One of them can outperform the method traditionally used in the field while using fewer channels, leading to even faster processing without compromising accuracy. This research lays the foundation for future advancements in onboard methane detection with minimal hardware requirements, improving timely data delivery. The produced code, data, and models are open-sourced and can be accessed from https://github.com/zaitra/methane-filters-benchmark.