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Fully Automatic Trace Gas Plume Detection
V\'it R\r{u} · 2026-05-06 · via cs.LG updates on arXiv.org

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Abstract:Future imaging spectrometers will increase data volumes by orders of magnitude, requiring automated detection of trace gas point sources. We present a fully automated framework that combines machine learning-based morphological analysis with physics-based spectroscopic fitting to detect plumes without human participation. Applied to EMIT imaging spectrometer data, the system operates in two modes: "daily digest" that runs automatically on all downlinked data, flagging the largest events for immediate response, and a retrospective analysis that identifies plumes missed by prior human review. The daily digest demonstrates that a significant fraction of the largest plumes can be detected automatically with negligible false positives, while retrospective analysis suggests at least 25% of plumes may have been overlooked. In addition to the previously observed methane point sources, we extend detection to three understudied trace gases: NH3, NO2 and the first observations of carbon monoxide (CO) plume in EMIT imagery.
Comments: Manuscript 27 pages, 9 figures, 1 table, more in attached supplementary; In review
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2605.03372 [cs.LG]
  (or arXiv:2605.03372v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2605.03372

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Vít Růžička [view email]
[v1] Tue, 5 May 2026 05:28:15 UTC (15,052 KB)