# On-board ML for Trace Gas detection in Imaging Spectroscopy data

arXiv: https://arxiv.org/abs/2609.04458 · HTML: https://mlsanity.com/paper/2609.04458/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 6 references that are in the map.
- First posted on arXiv 3 Sep 2026.

## Builds on (most cited first, 4 of 4)

- [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)
- [Fully Automatic Trace Gas Plume Detection](https://mlsanity.com/paper/2605.03372/) (2026)

## Abstract (arXiv)

Data collected during aerial and spaceborne imaging spectroscopy campaigns enables the detection of transient events such as trace gas emissions. However, current processing pipelines depend on slow, on-the-ground processing, which delays the time to information of each detected event and prohibits immediate follow-up actions. During the Tokyo Field Campaign of March 2026, we explored on-board processing of Imaging Spectroscopy data from the equipped AVIRIS-5 sensor. Due to communication bottlenecks, full datacubes cannot be downlinked immediately during the flight. Instead we downlink the potential events predicted by our efficient and small machine learning model. We show the first on-board detection of methane point source emission with Imaging Spectroscopy data using Edge ML.