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

Researchers demonstrated the first on-board detection of methane point source emissions using imaging spectroscopy data processed by a machine learning model during the Tokyo Field Campaign of March 2026, with data from the AVIRIS-5 sensor. The approach, described in a paper on arXiv (2609.04458v1), addresses communication bottlenecks by downlinking only predicted events instead of full datacubes, enabling faster response to trace gas emissions.

arXiv:2609.04458v1 Announce Type: new Abstract: 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.