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