Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 4
Articles | Volume 19, issue 4
https://doi.org/10.5194/gmd-19-1683-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-1683-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 4
Methods for assessment of models
 | 
27 Feb 2026
Methods for assessment of models |  | 27 Feb 2026

GHGPSE-Net: a method towards spaceborne automated extraction of greenhouse-gas point sources using point-object-detection deep neural network

Yiguo Pang, Denghui Hu, Longfei Tian, Shuang Gao, and Guohua Liu

Supplement

https://doi.org/10.5194/gmd-19-1683-2026-supplement

Data sets

Source code for "GHGPSE-Net: A method towards spaceborne automated extraction of greenhouse-gas point sources using point-object-detection deep neural network" Yiguo Pang https://doi.org/10.5281/zenodo.17417618

Model code and software

Source code for "GHGPSE-Net: A method towards spaceborne automated extraction of greenhouse-gas point sources using point-object-detection deep neural network" Yiguo Pang https://doi.org/10.5281/zenodo.17417618

WRF-GHG Simulation for "GHGPSE-Net: A method towards spaceborne automated extraction of greenhouse-gas point sources using point-object-detection deep neural network" Yiguo Pang https://doi.org/10.5281/zenodo.17337441

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Short summary
Satellites can reveal greenhouse gas point sources, but current point source extraction methods rely on manual inspection. We developed a point-object-detection-based deep learning method for fast, automated detection and quantification of these sources. The model was trained on a large synthetic dataset and tested for generalization using two independent datasets, including simulations and satellite observations.
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