EGU24-12242, updated on 09 Mar 2024
https://doi.org/10.5194/egusphere-egu24-12242
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under the Creative Commons Attribution 4.0 License.
Deep Learning-Based Monitoring of Artisanal Mining to Tackle Environmental Degradation: A SERVIR West Africa Case Study in Ghana
1,- 1International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Accra, Ghana (kidia.gelaye@icrisat.org)
- 2University of Ghana, Center for Remote Sensing and Geographic Information Services (CERSGIS), Accra, Ghana
- 3International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Dakar, Senegal
- 4Earth System Science Center, University of Alabama in Huntsville, Huntsville, AL, USA
- 5NASA SERVIR Science Coordination Office, Marshall Space Flight Center, Huntsville, AL, USA
How to cite: Gelaye, K., Asare, E., Amponsah, M., Bartel, P., Traore, P. C., Abramowitz, J., Cherrington, E., and Mensah, F.: Deep Learning-Based Monitoring of Artisanal Mining to Tackle Environmental Degradation: A SERVIR West Africa Case Study in Ghana, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12242, https://doi.org/10.5194/egusphere-egu24-12242, 2024.