Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 13
Articles | Volume 16, issue 13
https://doi.org/10.5194/gmd-16-3997-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-3997-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 13
Model description paper
 | 
14 Jul 2023
Model description paper |  | 14 Jul 2023

Segmentation of XCO2 images with deep learning: application to synthetic plumes from cities and power plants

Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Marc Bocquet, Jinghui Lian, Grégoire Broquet, Gerrit Kuhlmann, Alexandre Danjou, and Thomas Lauvaux

Supplement

https://doi.org/10.5194/gmd-16-3997-2023-supplement

Data sets

Synthetic XCO2, CO and NO2 observations for the CO2M and Sentinel-5 satellites Gerrit Kuhlmann, Valentin Clément, Julia Marshall, Oliver Fuhrer, Grégoire Broquet, Christina Schnadt-Poberaj, Armin Löscher, Yasjka Meijer, and Dominik Brunner https://doi.org/10.5281/zenodo.4048228

Model code and software

cerea-daml/co2-images-seg: XCO2 simulated satellite image segmentation paper (co2-seg-paper-sub) Joffrey Dumont Le Brazidec https://doi.org/10.5281/zenodo.7371413

Download
Short summary
Monitoring of CO2 emissions is key to the development of reduction policies. Local emissions, from cities or power plants, may be estimated from CO2 plumes detected in satellite images. CO2 plumes generally have a weak signal and are partially concealed by highly variable background concentrations and instrument errors, which hampers their detection. To address this problem, we propose and apply deep learning methods to detect the contour of a plume in simulated CO2 satellite images.
Read more