dronefreak/UAVid-2020

UAVid: Aerial Semantic Segmentation Dataset

Unofficial redistribution of the UAVid dataset under the original CC BY-NC-SA 4.0 license.

Disclaimer

This repository is not an official release of the UAVid dataset.

The UAVid dataset was created by the original authors, who retain all copyright and intellectual property rights. This repository does not claim ownership of any images, annotations, or metadata.

This repository exists for two purposes:

1. To reorganize the original dataset into a standardized YOLO/Ultralytics-compatible directory structure that can be used directly by many modern semantic segmentation training pipelines.
2. To provide a more reliable download source, as the original hosting server may be slow or difficult to access.

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Dataset Description

UAVid is a high-resolution semantic segmentation benchmark captured from low-altitude unmanned aerial vehicles (UAVs). The dataset contains urban scenes with pixel-level semantic annotations for semantic segmentation research.

This repository preserves the original dataset while packaging it in a standardized directory layout for improved compatibility with modern deep learning frameworks.

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Changes from the Official Release

This repository does not modify the dataset contents.

The following changes have been made:

• Reorganized the directory structure into a YOLO/Ultralytics-compatible layout.
• Added a data.yaml configuration file for easier integration with Ultralytics-based projects.
• Preserved the original train, validation, and test splits.
• Preserved all original filenames.
• Preserved all original images.
• Preserved all original segmentation masks.
• Preserved all annotations.
• No labels were changed.
• No samples were added or removed.

Apart from the directory organization and configuration file, the dataset contents are identical to the official release.

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Dataset Structure

dataset/
├── README.md
├── data.yaml
├── images/
│   ├── train/
│   ├── val/
│   └── test/
└── masks/
    ├── train/
    ├── val/
    └── test/

where:

• images/ contains the original RGB UAV images organized by dataset split.
• masks/ contains the corresponding semantic segmentation masks for each split.
• data.yaml is a configuration file added in this repository to simplify loading the dataset in Ultralytics-compatible training pipelines.
• The original train, validation, and test splits have been preserved exactly as released by the UAVid authors.

Each image in images/<split>/ has a corresponding segmentation mask with the same filename in masks/<split>/.

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Dataset Sources

Original Paper

UAVid: A Semantic Segmentation Dataset for UAV Imagery

Ye Lyu, George Vosselman, Gui-Song Xia, Alper Yilmaz, Michael Ying Yang

ISPRS Journal of Photogrammetry and Remote Sensing, Volume 165, 2020, Pages 108–119.

DOI: https://doi.org/10.1016/j.isprsjprs.2020.05.009

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Official Resources

• Official Website: https://uavid.nl/
• Official Dataset Archive: https://doi.org/10.17026/dans-x9f-w9sa
• Paper: https://arxiv.org/abs/1810.10438
• Published Journal: https://doi.org/10.1016/j.isprsjprs.2020.05.009

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Attribution

All credit for the dataset belongs entirely to the original UAVid authors.

This repository only redistributes the original dataset under the same license while reorganizing the directory structure for improved usability and accessibility.

If you use this dataset in your research, please cite the original publication below.

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License

The original UAVid dataset is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

Accordingly:

• Attribution to the original authors is required.
• Commercial use is prohibited.
• Any derivative work must be distributed under the same license.

This repository is distributed under the same license.

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Citation

If you use this dataset, please cite:

@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 = {10.1016/j.isprsjprs.2020.05.009}
}

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Acknowledgements

We sincerely thank the original UAVid authors for creating and publicly releasing this valuable benchmark, which has significantly contributed to research in semantic segmentation for UAV imagery.