Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 9
Articles | Volume 17, issue 9
https://doi.org/10.5194/amt-17-2583-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-2583-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 9
Research article
 | 
03 May 2024
Research article |  | 03 May 2024

CH4Net: a deep learning model for monitoring methane super-emitters with Sentinel-2 imagery

Anna Vaughan, Gonzalo Mateo-García, Luis Gómez-Chova, Vít Růžička, Luis Guanter, and Itziar Irakulis-Loitxate

Viewed

Total article views: 2,066 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,297 708 61 2,066 53 49
  • HTML: 1,297
  • PDF: 708
  • XML: 61
  • Total: 2,066
  • BibTeX: 53
  • EndNote: 49
Views and downloads (calculated since 09 May 2023)
Cumulative views and downloads (calculated since 09 May 2023)

Viewed (geographical distribution)

Total article views: 2,066 (including HTML, PDF, and XML) Thereof 2,017 with geography defined and 49 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 17 Jul 2024
Download
Short summary
Methane is a potent greenhouse gas that has been responsible for around 25 % of global warming since the industrial revolution. Consequently identifying and mitigating methane emissions comprise an important step in combating the climate crisis. We develop a new deep learning model to automatically detect methane plumes from satellite images and demonstrate that this can be applied to monitor large methane emissions resulting from the oil and gas industry.
Read more