Project-AgML/wetveg2mm_riparian_vegetation_classification

Wetveg2mm Riparian Vegetation Classification

This dataset provides ultra-high-resolution RGB imagery capturing riparian vegetation in a field environment along the Jiuzhou River in Guangxi, China. Collected using a DJI Matrice 350 RTK UAV equipped with a SHARE 100M A10 medium-format metric camera during October 2025, it offers a real-world resource for fine-grained semantic segmentation of vegetation structures. The dataset contains 1,632 images with pixel-level mask annotations.

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

The original train/test/val split has been preserved in the split column.

Citation

@article{liu2026wetveg,
  title={WetVeg-2mm: An Ultra-High-Resolution UAV Dataset for Riparian Vegetation Semantic Segmentation},
  author={Liu, Guiqi and Zhang, Runqiao and Qin, Huapeng},
  journal={Remote Sensing},
  volume={18},
  pages={2508},
  year={2026},
  publisher={MDPI}
}

The dataset itself can be cited as:

Liu, G., Zhang, R., & Qin, H. (2026). WetVeg-2mm: An Ultra-High-Resolution UAV Orthomosaic Dataset for Fine-Grained Riparian Vegetation Semantic Segmentation (Version v1.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.19849119

This dataset was reformatted from its original format to match HuggingFace standards.