Papers › VDD: Varied Drone Dataset for Semantic Segmentation
VDD: Varied Drone Dataset for Semantic Segmentation
Wenxiao Cai, Ke Jin, Jinyan Hou, Cong Guo, Letian Wu, Wankou Yang
Semantic segmentation of drone images is critical for various aerial vision tasks as it provides essential semantic details to understand scenes on the ground. Ensuring high accuracy of semantic segmentation models for drones requires access to diverse, large-scale, and high-resolution datasets, which are often scarce in the field of aerial image processing. While existing datasets typically focus on urban scenes and are relatively small, our Varied Drone Dataset (VDD) addresses these limitations by offering a large-scale, densely labeled collection of 400 high-resolution images spanning 7 classes. This dataset features various scenes in urban, industrial, rural, and natural areas, captured from different camera angles and under diverse lighting conditions. We also make new annotations to UDD and UAVid, integrating them under VDD annotation standards, to create the Integrated Drone Dataset (IDD). We train seven state-of-the-art models on drone datasets as baselines. It's expected that our dataset will generate considerable interest in drone image segmentation and serve as a foundation for other drone vision tasks. Datasets are publicly available at \href{our website}{https://github.com/RussRobin/VDD}.
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Code
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Tasks
Datasets
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | VDD | Segformer-B2 | mIoU | 85.75 | #1 of 7 | Archive leaderboard | report |
| Semantic Segmentation | VDD | UperNet(Swin-L) | mIoU | 85.63 | #2 of 7 | Archive leaderboard | report |
| Semantic Segmentation | VDD | UperNet(Swin-T) | mIoU | 84.73 | #3 of 7 | Archive leaderboard | report |
| Semantic Segmentation | VDD | Mask2Former(ResNet-50) | mIoU | 83.21 | #4 of 7 | Archive leaderboard | report |
| Semantic Segmentation | VDD | Segformer-B5 | mIoU | 82.11 | #5 of 7 | Archive leaderboard | report |
| Semantic Segmentation | VDD | Mask2Former(Swin-T) | mIoU | 77.85 | #6 of 7 | Archive leaderboard | report |
| Semantic Segmentation | VDD | Segformer-B0 | mIoU | 75.37 | #7 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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