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.
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.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
05 Mar 2024
Model description paper |
| 05 Mar 2024
| 05 Mar 2024
Deep learning applied to CO2 power plant emissions quantification using simulated satellite images
Joffrey Dumont Le Brazidec
CORRESPONDING AUTHOR
CEREA, École des Ponts and EDF R&D, Île-de-France, France
Pierre Vanderbecken
CEREA, École des Ponts and EDF R&D, Île-de-France, France
Alban Farchi
CEREA, École des Ponts and EDF R&D, Île-de-France, France
Grégoire Broquet
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91198 Gif-sur-Yvette, France
Gerrit Kuhlmann
Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, Switzerland
Marc Bocquet
CEREA, École des Ponts and EDF R&D, Île-de-France, France
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Cited
20 citations as recorded by crossref.
- CH4Vision: Machine Learning Estimation of Methane Flux with GaoFen-5 Hyperspectral Imagery K. Li et al. https://doi.org/10.34133/remotesensing.1013
- The ddeq Python library for point source quantification from remote sensing images (version 1.0) G. Kuhlmann et al. https://doi.org/10.5194/gmd-17-4773-2024
- Benchmarking data-driven inversion methods for the estimation of local CO2 emissions from synthetic satellite images of XCO2 and NO2 D. Santaren et al. https://doi.org/10.5194/amt-18-211-2025
- Near-real-time inversion of SO₂ emission via coupled three-dimensional variational assimilation and deep learning surrogate framework over China H. Ye et al. https://doi.org/10.1016/j.atmosres.2026.109151
- Bridging accuracy and interpretability: Comparative insights from interpretable and black-box models for CO₂ emission forecasting H. P. M. et al. https://doi.org/10.18686/cest530
- 2024 ESA-ECMWF workshop report: current status, progress and opportunities in machine learning for Earth system observation and prediction P. Ebel et al. https://doi.org/10.1038/s41612-024-00757-4
- The Downscaling Prediction Algorithm of Traffic Source Carbon Emissions Based on Multisource Remote Sensing Data and Deep Learning L. Zheng et al. https://doi.org/10.1109/TGRS.2025.3628964
- AI-Driven greenhouse gas monitoring: enhancing accuracy, efficiency, and real-time emissions tracking R. Hasan et al. https://doi.org/10.3934/environsci.2025023
- Estimation of Fossil Fuel Carbon Emissions in Mainland China by Incorporating Multiple Spatiotemporal Data and Machine Learning Algorithms L. Zheng et al. https://doi.org/10.1109/TGRS.2025.3648355
- Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods J. Dumont Le Brazidec et al. https://doi.org/10.5194/gmd-18-3607-2025
- An Explainable Multi-Stage Feature Selection Framework for Power-Station CO2 Emissions Forecasting M. Qader & F. Albalooshi https://doi.org/10.3390/en19051210
- Monitoring fossil fuel CO2 emissions from co-emitted NO2 observed from space: progress, challenges, and future perspectives H. Li et al. https://doi.org/10.1007/s11783-025-1922-x
- Ai-driven carbon Monitoring: Transformer-Based reconstruction of atmospheric CO2 in Canadian poultry regions P. Prajesh et al. https://doi.org/10.1016/j.jag.2026.105248
- GHGPSE-Net: a method towards spaceborne automated extraction of greenhouse-gas point sources using point-object-detection deep neural network Y. Pang et al. https://doi.org/10.5194/gmd-19-1683-2026
- Leveraging wide snapshot XCO2 pre-training to estimate urban fossil fuel CO2 emissions from space Z. Wang et al. https://doi.org/10.1016/j.rse.2026.115260
- Predominantly positive XCO2 anomalies in the Caatinga biome highlight carbon vulnerability L. Silva et al. https://doi.org/10.1038/s41598-026-37629-1
- Comparative multi-algorithm AI framework for real-time carbon emission optimization in a medium-scale irrigation project in Thailand C. Mueangphaen et al. https://doi.org/10.1016/j.eiar.2025.108276
- A Top-Down Method for Estimating Regional Fossil Fuel Carbon Emissions Based on Satellite XCO2 Retrievals L. Zhang et al. https://doi.org/10.3390/rs17030447
- La surveillance des émissions anthropiques de CO2 depuis l’espace : un enjeu géopolitique émergent G. Broquet & F. Chevallier https://doi.org/10.4000/12de3
- Improvements of AI-driven emission estimation for point sources applied to high resolution 2-D methane-plume imagery T. Plewa et al. https://doi.org/10.1016/j.rse.2025.115002
20 citations as recorded by crossref.
- CH4Vision: Machine Learning Estimation of Methane Flux with GaoFen-5 Hyperspectral Imagery K. Li et al. https://doi.org/10.34133/remotesensing.1013
- The ddeq Python library for point source quantification from remote sensing images (version 1.0) G. Kuhlmann et al. https://doi.org/10.5194/gmd-17-4773-2024
- Benchmarking data-driven inversion methods for the estimation of local CO2 emissions from synthetic satellite images of XCO2 and NO2 D. Santaren et al. https://doi.org/10.5194/amt-18-211-2025
- Near-real-time inversion of SO₂ emission via coupled three-dimensional variational assimilation and deep learning surrogate framework over China H. Ye et al. https://doi.org/10.1016/j.atmosres.2026.109151
- Bridging accuracy and interpretability: Comparative insights from interpretable and black-box models for CO₂ emission forecasting H. P. M. et al. https://doi.org/10.18686/cest530
- 2024 ESA-ECMWF workshop report: current status, progress and opportunities in machine learning for Earth system observation and prediction P. Ebel et al. https://doi.org/10.1038/s41612-024-00757-4
- The Downscaling Prediction Algorithm of Traffic Source Carbon Emissions Based on Multisource Remote Sensing Data and Deep Learning L. Zheng et al. https://doi.org/10.1109/TGRS.2025.3628964
- AI-Driven greenhouse gas monitoring: enhancing accuracy, efficiency, and real-time emissions tracking R. Hasan et al. https://doi.org/10.3934/environsci.2025023
- Estimation of Fossil Fuel Carbon Emissions in Mainland China by Incorporating Multiple Spatiotemporal Data and Machine Learning Algorithms L. Zheng et al. https://doi.org/10.1109/TGRS.2025.3648355
- Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods J. Dumont Le Brazidec et al. https://doi.org/10.5194/gmd-18-3607-2025
- An Explainable Multi-Stage Feature Selection Framework for Power-Station CO2 Emissions Forecasting M. Qader & F. Albalooshi https://doi.org/10.3390/en19051210
- Monitoring fossil fuel CO2 emissions from co-emitted NO2 observed from space: progress, challenges, and future perspectives H. Li et al. https://doi.org/10.1007/s11783-025-1922-x
- Ai-driven carbon Monitoring: Transformer-Based reconstruction of atmospheric CO2 in Canadian poultry regions P. Prajesh et al. https://doi.org/10.1016/j.jag.2026.105248
- GHGPSE-Net: a method towards spaceborne automated extraction of greenhouse-gas point sources using point-object-detection deep neural network Y. Pang et al. https://doi.org/10.5194/gmd-19-1683-2026
- Leveraging wide snapshot XCO2 pre-training to estimate urban fossil fuel CO2 emissions from space Z. Wang et al. https://doi.org/10.1016/j.rse.2026.115260
- Predominantly positive XCO2 anomalies in the Caatinga biome highlight carbon vulnerability L. Silva et al. https://doi.org/10.1038/s41598-026-37629-1
- Comparative multi-algorithm AI framework for real-time carbon emission optimization in a medium-scale irrigation project in Thailand C. Mueangphaen et al. https://doi.org/10.1016/j.eiar.2025.108276
- A Top-Down Method for Estimating Regional Fossil Fuel Carbon Emissions Based on Satellite XCO2 Retrievals L. Zhang et al. https://doi.org/10.3390/rs17030447
- La surveillance des émissions anthropiques de CO2 depuis l’espace : un enjeu géopolitique émergent G. Broquet & F. Chevallier https://doi.org/10.4000/12de3
- Improvements of AI-driven emission estimation for point sources applied to high resolution 2-D methane-plume imagery T. Plewa et al. https://doi.org/10.1016/j.rse.2025.115002
Saved (final revised paper)
Latest update: 17 Jul 2026
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.
Our research presents an innovative approach to estimating power plant CO2 emissions from...
