UAVid-3D-Scenes

March 2, 2026 · View on GitHub

UAVid-3D-Scenes is a depth-estimation centric extension for the UAVid semantic dataset, organizing the original sequences based on the larger scenes they were captured in, providing undistorted RGB frames paired with metric depth maps obtained through COLMAP reconstructions and scaling.

🤗 Data is accessible on the projects huggingface repository https://huggingface.co/datasets/hrflr/uavid-3d-scenes

📃 This dataset accompanies the paper TanDepth: Leveraging Global DEMs for Metric Monocular Depth Estimation in UAVs

License: CC BY-NC-SA 4.0 Creative Commons Attribution-NonCommercial-ShareAlike 4.0

If you use this dataset in academic work, please cite the TanDepth paper and the original UAVid dataset.

TanDepth (IEEE J-STARS, 2025)

@ARTICLE{TanDepth2025,
  author={Florea, Horatiu and Nedevschi, Sergiu},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  title={TanDepth: Leveraging Global DEMs for Metric Monocular Depth Estimation in UAVs},
  year={2025},
  volume={18},
  pages={5445-5459},
  doi={10.1109/JSTARS.2025.3531984}
  url={https://ieeexplore.ieee.org/abstract/document/10848130}
}

UAVid (source RGB data)

@article{LYU2020108,
	author = "Ye Lyu and George Vosselman and Gui-Song Xia and Alper Yilmaz and Michael Ying Yang",
	title = "UAVid: A semantic segmentation dataset for UAV imagery",
	journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
	volume = "165",
	pages = "108 - 119",
	year = "2020",
	issn = "0924-2716",
	doi = "https://doi.org/10.1016/j.isprsjprs.2020.05.009",
	url = {http://www.sciencedirect.com/science/article/pii/S0924271620301295},
}