computer vision

PlumeQuant: Uncertainty-aware consistency assessment of methane plume masks and emission-rate estimates

arXiv:2607.13945

summary

The paper presents PlumeQuant, a framework that quantifies uncertainty and consistency of methane plume masks and derived emission rates by generating alternative plausible masks with genetic‑algorithm ensembles and comparing them to reference masks.

Abstract

Imaging spectrometers increasingly distribute source-resolved methane plume products in which the plume mask, integrated mass enhancement (IME), plume length, emission rate, and uncertainty are physically and algorithmically linked. Using 63 EMIT-derived Carbon Mapper plume records from 27 scenes, we show that these published scalar quantities do not uniquely constrain the plume boundary: substantially different yet plausible masks reproduce the same IME, plume length, and emission rate. Genetic-algorithm (GA) ensembles conditioned on the published IME and plume length make this equifinality explicit: the high-confidence core selected by nearly all target-consistent masks covers a median of 13% of the plausible footprint envelope, and ambiguity is largest for weak, low-overlap plumes. The diagnostics come from PlumeQuant, which recomputes IME, plume length, emission rate, and five-term uncertainty from distributed product components under stated conventions and evaluates four mask representations: the distributed reference mask, a transparent Carbon Mapper-informed analogue (CM-like), the GA ensemble, and optional expert edits. The CM-like mask is generated per plume without access to the reference mask or published quantities, with settings fixed once on a scene-disjoint 44-plume development split. It reproduced published IME with +0.72% median difference and emission rate with +0.16% (6.98% mean absolute), reached 0.843 median intersection-over-union against the reference masks, and matched the published uncertainty scale (median ratio 1.01). Holdout mean absolute errors were 7.6% (IME), 9.5% (length), and 6.1% (rate). These are product-level consistency diagnostics, not independent validation. They flag weak, offset, or ambiguous plumes for expert review.

50 pages, 9 figures. Supplementary material provided as an ancillary file. Code and derived data archived at https://doi.org/10.5281/zenodo.21282534

Topics & keywords

#methane plume detection#mask uncertainty quantification#genetic algorithm ensembles#remote sensing#emission rate estimationplume maskintegrated mass enhancementemission rategenetic algorithmuncertainty assessmentEMITCarbon Mapper