Fully Automatic Trace Gas Plume Detection
Future imaging spectrometers will expand contemporary data volumes by orders of magnitude, requiring automated methods to upscale labor-intensive detection of trace gas point sources. Here we present a fully-automated approach that achieves operational performance for plume detection and labelling without human participation. Our method combines machine learning (ML)-based morphological analysis with physics-based spectroscopic model fitting. We deploy it on data from the EMIT imaging spectrometer, operating in two modes. First, we present a "daily digest" that runs automatically on all downlinked data, flagging the largest events for immediate response. The daily digest demonstrates that...
| Authors | Vít Růžička, David R. Thompson, Jay E. Fahlen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 5 May 2026 |
| Last updated at source | 27 July 2026 |
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