UAVid++: Higher-Quality Labels and Expanded Semantic Taxonomy for Aerial Semantic Segmentation

High-quality annotated datasets are critical for semantic segmentation, particularly in aerial imagery, where the number of manually annotated frames is limited and precise labeling is often missing. To address this, we introduce UAVid++, a semantically refined and expanded version of the UAVid dataset for aerial semantic segmentation. The proposed dataset provides higher quality annotations through manual corrections addressing labeling errors across the entire original UAVid dataset. In addition, we expand the taxonomy with three finely annotated semantic classes, Water, Sky, and Roof, selected to enrich the semantic space and separate visually distinct objects previously grouped together under the same label. We evaluated state-of-the-art aerial segmentation methods on UAVid, UAVid+ (with corrections, without expanded taxonomy), and the full UAVid++ dataset. Corrected annotations improved both training and evaluation performance by up to 7.4% mean Intersection over Union (mIoU), and adding the three classes further increased performance by up to 4.1%, demonstrating the combined impact of refined labels and the expanded semantic taxonomy. UAVid++ enables more discriminative evaluation of state-of-the-art methods and improves their relative ranking. We adapt frozen DINO-pretrained Vision Transformer backbones using trainable task-specific heads for aerial semantic dataset semantic segmentation. This strategy achieves strong segmentation performance and robust generalization while quantifying the gains from improved labels relative to pretrained backbone capacity. Relative to the best-performing state-of-the-art method, our configurations achieve a 2.7% mIoU improvement on UAVid++, up to 12.4% mIoU gains on out-of-distribution aerial semantic datasets, and a 38.85% mIoU gain on a cross-domain dataset. Both the dataset and implementation will be publicly available athttps://vivichiciudean.github.io/uavidplusplus/.