U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers

Jack Bruno, Dylan Jervis, Daniel J. Varon, Daniel J. Jacob

Atmospheric measurement techniques 2024 article cited by 39

importance 7/10 partially solved Atmospheric and Environmental Gas Dynamics emissions monitoring stage: research

Detecting atmospheric methane plumes and quantifying point-source rates from growing high-resolution satellite imagery is labor-intensive and not scalable.

Scalable monitoring of methane point sources is needed to keep pace with expanding satellite datasets.

U-Plume uses a U-Net architecture to segment and mask plumes, then quantifies source rates with a CNN or a physics-based integrated mass enhancement method using wind speed data.

Processes 62 images (128×128 pixels) per second on a single 2.6 GHz Intel Core i7-9750H CPU.

Provides an automated, scalable pipeline for plume detection and source quantification from satellite imagery.

Venue Atmospheric measurement techniques
Published 2024
Work type article
Authors Jack Bruno, Dylan Jervis, Daniel J. Varon, Daniel J. Jacob
Citations 39 (OpenAlex, at import time)
References 39
Model or method U-Net; convolutional neural network (CNN)
Organization not stated in source
Primary source https://doi.org/10.5194/amt-17-2625-2024
Open access gold
Keywords U-Net, methane plume, satellite imagery, source quantification, CNN, large-eddy simulation
7 / 10

significant — thousands to millions affected

Scalable automated monitoring of methane point sources supports emissions reduction amid growing satellite datasets; abstract frames the scalability gap but states no quantified climate or source scale.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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