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Abstract 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.
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