WetVeg-2mm: An Ultra-High-Resolution UAV Dataset for Riparian Vegetation Semantic Segmentation

Abstract

Fine-grained mapping of riparian vegetation is important for ecological monitoring, invasive species control, and ecosystem restoration. However, riparian plant communities often exhibit fragmented patches, broad transition zones and high visual similarity among classes, making stable species-level segmentation difficult from conventional satellite imagery or lower-resolution UAV imagery. To address this gap, we present WetVeg-2mm, an ultra-high-resolution UAV dataset for fine-grained riparian vegetation semantic segmentation. Built from UAV surveys over a representative riparian section of the Jiuzhou River in Guangxi, China, the dataset provides 2054 image chips (1024 × 1024) with pixel-level annotations at 2 mm ground sampling distance. It contains 17 semantic classes in total, including 14 representative wetland plant classes, such as Colocasia, Eichhornia and Phragmites, together with water, bareland and background. Five baseline models, namely U-Net, Attention U-Net, DeepLabV3+, PSPNet and SegFormer, were evaluated using per-class IoU, mIoU, mDice, PA, Precision and Recall. Across all evaluated baseline settings, SegFormer with ImageNet pretraining achieved the best overall performance, with 76.23% mIoU, 86.01% mDice, 85.17% PA, 87.30% Recall and 85.42% Precision on the test set. Overall, WetVeg-2mm provides a reproducible and challenging benchmark for fine-grained riparian vegetation semantic segmentation.