Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 5
Articles | Volume 17, issue 5
https://doi.org/10.5194/gmd-17-1995-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-1995-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 5
Model description paper
 | 
05 Mar 2024
Model description paper |  | 05 Mar 2024

Deep learning applied to CO2 power plant emissions quantification using simulated satellite images

Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet

Viewed

Total article views: 3,376 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,354 930 92 3,376 102 106 144
  • HTML: 2,354
  • PDF: 930
  • XML: 92
  • Total: 3,376
  • Supplement: 102
  • BibTeX: 106
  • EndNote: 144
Views and downloads (calculated since 26 Jul 2023)
Cumulative views and downloads (calculated since 26 Jul 2023)

Viewed (geographical distribution)

Total article views: 3,376 (including HTML, PDF, and XML) Thereof 3,303 with geography defined and 73 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 08 Nov 2025
Download
Short summary
Our research presents an innovative approach to estimating power plant CO2 emissions from satellite images of the corresponding plumes such as those from the forthcoming CO2M satellite constellation. The exploitation of these images is challenging due to noise and meteorological uncertainties. To overcome these obstacles, we use a deep learning neural network trained on simulated CO2 images. Our method outperforms alternatives, providing a positive perspective for the analysis of CO2M images.
Read more
Share