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.
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.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
27 Feb 2026
Methods for assessment of models |
| 27 Feb 2026
| 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
Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai, China
University of Chinese Academy of Sciences, Beijing, China
Denghui Hu
Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai, China
Longfei Tian
Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai, China
Shuang Gao
Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai, China
Guohua Liu
CORRESPONDING AUTHOR
Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai, China
University of Chinese Academy of Sciences, Beijing, China
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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.
Satellites can reveal greenhouse gas point sources, but current point source extraction methods...
