Deep learning for wetland vegetation mapping in the southeastern United States: evaluating site-specific accuracy and classification challenges
- DOI
- 10.1038/s41598-026-57454-w
- Published
- 2026-06-26
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-57454-w,
title = {Deep learning for wetland vegetation mapping in the southeastern United States: evaluating site-specific accuracy and classification challenges},
author = {Obioma C. Ogwo and Hamdi A. Zurqani and Douglas C. Osborne and Anne E. Mini and Ryan J. Askren and Keith McKnight},
year = {2026},
journal = {Scientific Reports},
doi = {10.1038/s41598-026-57454-w},
url = {https://doi.org/10.1038/s41598-026-57454-w}
}RIS
TY - JOUR TI - Deep learning for wetland vegetation mapping in the southeastern United States: evaluating site-specific accuracy and classification challenges AU - Obioma C. Ogwo AU - Hamdi A. Zurqani AU - Douglas C. Osborne AU - Anne E. Mini AU - Ryan J. Askren AU - Keith McKnight PY - 2026 JO - Scientific Reports DO - 10.1038/s41598-026-57454-w UR - https://doi.org/10.1038/s41598-026-57454-w ER -
APA
Ogwo, O. C., Zurqani, H. A., Osborne, D. C., Mini, A. E., Askren, R. J., & McKnight, K. (2026). Deep learning for wetland vegetation mapping in the southeastern United States: evaluating site-specific accuracy and classification challenges. Scientific Reports. https://doi.org/10.1038/s41598-026-57454-w
Source records
- crossref · retrieved 2026-11-06T22:30:00.642Z