What is UAVid?

The UAVid dataset is an UAV video dataset for semantic segmentation task focusing on urban scenes. It has several features:

  • Semantic segmentation
  • 4K resolution UAV videos
  • 8 object categories
  • Street scene context

High resolution quality


The images are captured in very high resolution with detailed scenes.

What are the categories?


There are 8 categories in total:

  • Building
  • Road
  • Static car
  • Tree
  • Low vegetation
  • Human
  • Moving car
  • Background clutter

News

Copyright

UAVid dataset and UAVid-depth dataset are copyright by us and published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License. This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license.

Citation

Please cite our paper if you find our UAVid dataset useful.
Bibtex references are as follows,

@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",
}

When using the UAVid-depth dataset in your research, please cite:
@article{uaviddepth21,
Author = {Logambal Madhuanand and Francesco Nex and Michael Ying Yang},
Title = {Self-supervised monocular depth estimation from oblique UAV videos},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
year = {2021},
volume = {176},
pages = {1-14},
}

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Organization

UT            WHU            OSU

  • Semantic Labelling with Video Support

    • The UAVid dataset provides images and labels for the training and validation set, and images only for the testing set.

    All sequences are provided with the corresponding videos. Image and label files are named according to the 0-based index in the video sequence.

    EOStore
    (password: uavid2020)     Baidu Pan
    (password: 70fo)

  • Semantic Labelling with Images Only

    • If you only need images and labels from the UAVid dataset without video support, please use the following link.

    EOStore
    (password: uavid2020)     Baidu Pan
    (password: rart)

  • UAVid Toolkit (python)

    • UAVidToolKit provides basic tools for easier usage of the UAVid dataset. Including label conversion, label visualization, performance evaluation

    UAVidToolKit github page

  • Semantic Labelling

    • The task for UAVid dataset is to predict per-pixel semantic labelling for the UAV video sequences. The original video file for each sequence is provided together with the labelled images. Currently, UAVid only supports image level semantic labelling without instance level consideration.

  • Evaluation Metric

    • The semantic labelling performance is assessed based on the standard Jaccard Index, more known as the PASCAL VOC intersection-over-union metric.

    $$IoU = {TP \over TP+FP+FN}.$$

    TP, FP and FN are the numbers of true positive, false positive and false negative respectively, which can be calculated through the confusion matrix determined over all data from test split.
    The goal for this task is to achieve as high IoU score as possible. For UAVid dataset, clutter class has a relatively large pixel number ratio and consists of meaningful objects, which is taken as one class for both training and evaluation rather than being ignored.