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

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.

Machine Learning (cs.LG); Performance (cs.PF)

arXiv:2609.04458 [cs.LG]

(or arXiv:2609.04458v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.04458 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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