Evaluation of U-Net Variants and Traditional Machine Learning Methods for Land Cover Classification Using High-Resolution Satellite Imagery

Ebrahimi, Aydin; Ghourkhanehchi Zirak, Amirhossein; Sedaghat, Amin; Mohammadi, Nazila

doi:10.5194/isprs-annals-X-4-W8-2025-205-2026

Articles | Volume X-4/W8-2025

https://doi.org/10.5194/isprs-annals-X-4-W8-2025-205-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/isprs-annals-X-4-W8-2025-205-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume X-4/W8-2025

29 May 2026

| 29 May 2026

Evaluation of U-Net Variants and Traditional Machine Learning Methods for Land Cover Classification Using High-Resolution Satellite Imagery

Aydin Ebrahimi, Amirhossein Ghourkhanehchi Zirak, Amin Sedaghat, and Nazila Mohammadi

Metrics will be available soon.

Latest update: 30 May 2026

The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.

Useful Links

Useful External Links

Our Contact

ISPRS
c/o
Leibniz University Hannover
Institute of Photogrammetry and GeoInformation
Nienburger Str. 1
D-30167 Hannover
GERMANY
Email: isprs-sg@isprs.org