U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers
Problem
Detecting atmospheric methane plumes and quantifying point-source rates from growing high-resolution satellite imagery is labor-intensive and not scalable.
Why it matters
Scalable monitoring of methane point sources is needed to keep pace with expanding satellite datasets.
What the AI did
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.
Results
Processes 62 images (128×128 pixels) per second on a single 2.6 GHz Intel Core i7-9750H CPU.
Impact
Provides an automated, scalable pipeline for plume detection and source quantification from satellite imagery.
Record
| 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 |
Importance rating
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.
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