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 Jun 26
- Container
- Scientific reports
- Publisher
- Not recorded
- Open access
- no
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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 = {Ogwo OC and Zurqani HA and Osborne DC and Mini AE and Askren RJ and McKnight K},
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 - Ogwo OC AU - Zurqani HA AU - Osborne DC AU - Mini AE AU - Askren RJ AU - McKnight K PY - 2026 JO - Scientific reports DO - 10.1038/s41598-026-57454-w UR - https://doi.org/10.1038/s41598-026-57454-w ER -
APA
OC, O., HA, Z., DC, O., AE, M., RJ, A., & K, M. (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
- pubmed · retrieved 2026-09-26T08:18:07.730Z
- europe-pmc · retrieved 2026-09-26T08:18:07.757Z