Evaluation

Data Format

You have to provide a single zip containing the label.

The contents of the zip-file should be organized like this:

    zip  
    ├── description.txt (optional)
    ├── birmingham_block_2.label
    ├── birmingham_block_8.label
    ├── cambridge_block_15.label
    ├── cambridge_block_16.label
    ├── cambridge_block_22.label
    └── cambridge_block_27.label

Please include a description.txt file with the following content:

method name: 
method description: 
project url: 
publication url: 
bibtex: 
organization or affiliation: 
email:

Important: Submitting the description.txt is required to get your result evaluated by our server. Submissions without method descriptions will not be considered in the final competition, not eligible for the prize award, and the result will be removed from the leaderboard. If the approach has been previously published, please include the publication URL, a detailed description of any improvements made, and the parameters used in this competition. Please also include any data augmentation techniques used, challenges, and issues you were facing.

It is strongly recommended to use the verification script of the SensatUrban API (available at github), since all submissions count towards the overall maximum number of submissions.

Important: Select the appropriate "phase" for your method to get the appropriate final result averaged over the correct number of classes.

Note: The upload of the zip file with your results takes some time and there is (unfortunately) no indicator for the status of the upload. You will just see that is being processed upon successful uploading your data.

Evaluation Criterion

To assess the labeling performance, we rely on the commonly applied mean Jaccard Index or mean intersection-over-union (mIoU) metric over all classes.

We use a total of 13 semantic classes during training and testing, including ground, vegetation, building, wall, bridge, parking, rail, car, footpath, bike, water, traffic road, and street furniture.

Terms and Conditions

Submission Policy

Only the training set is provided for learning the parameters of the algorithms. The test set should be used only for reporting the final results compared to other approaches - it must not be used in any way to train or tune systems, for example, by evaluating multiple parameters or feature choices and reporting the best results obtained. Thus, we impose an upper limit (currently 5 attempts) on the number of submissions. It is the participant's responsibility to divide the training set into proper training and validation splits. The tuned algorithms should then be run - ideally - only once on the test data and the results of the test set should not be used to adapt the approach.

The evaluation server may not be used for parameter tuning since we rely here on a shared resource that is provided by the Codalab team and its sponsors. We ask each participant to upload the final results of their algorithm/paper submission only once to the server and perform all other experiments on the validation set. If participants would like to report results in their papers for multiple versions of their algorithm (e.g., parameters or features), this must be done on the validation data and only the best performing setting of the novel method may be submitted for evaluation to our server. If comparisons to baselines from third parties (which have not been evaluated on the benchmark website) are desired, please contact us for a discussion.

Important note: It is NOT allowed to register multiple times to the server using different email addresses. We are actively monitoring submissions and we will revoke access and delete submissions. When registering with Codalab, we ask all participants to use a unique institutional email address (e.g., .edu) or company email address. We will not approve email addresses from free email services anymore (e.g., gmail.com, hotmail.com, qq.com). If you need to use such an email address, then contact us to approve your account.

License

The provided dataset is based on the data under Creative Commons Attribution-NonCommercial-ShareAlike license and all underlying data remains at all times the property of Sensat Ltd and/or other affiliated Sensat entities. You are free to share and adapt the data, but have to give appropriate credit and may not use the work for commercial purposes.

Specifically, you should consider citing our work:

@inproceedings{hu2020towards,
  title={Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges},
  author={Hu, Qingyong and Yang, Bo and Khalid, Sheikh and Xiao, Wen and Trigoni, Niki and Markham, Andrew},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2021}
}

For more information, please visit our project page at https://github.com/QingyongHu/SensatUrban.

How to Participate

Before you can submit your first results, you need to register with CodaLab and login to participate. Only then you can submit results to the evaluation server, which will score your submission on the non-public test set.

Important note: It is NOT allowed to register multiple times to the server using different email addresses. We are actively monitoring submissions and we will revoke access and delete submissions. When registering with Codalab, we ask all participants to use their unique institutional email address (e.g., .edu) or company email address. We will not approve email addresses from free email services anymore (e.g., gmail.com, hotmail.com, qq.com). If you need to use such an email address, then contact us to approve your account.

Steps

1. Prepare your submission in the required format, as described under the Evaluation section. CodaLab expects you to upload a single zip.
2. Use the validation script from the sensaturban-api to ensure that the folder structure and number of label files in the zip file is correct. All submissions count towards the overall maximum number of submissions!
3. Go to Participate and the Submit / View Results page.
4. Select the appropriate phase, i.e., Fully-supervised or Weakly-supervised, for which you computed the results.
5. Enter the required fields, where you can supply also later more details, if you need to take care of anonymity in case of double blind submissions.
6. Then you have to click "Submit" in the lower part of the page, which will open a file dialog. In the file dialog, you have to select your submission zip file, which will be then uploaded.
   Important: Don't close the window or tab until you see that a row has been added in the table under the "submit" button.
7. The evaluation takes roughly 10 minutes to complete and you will have the choice, which of your submission gets added to the leaderboard.

Good luck with your submission!

Qingyong Hu, University of Oxford

Meida Chen, University of Southern California - Institute for Creative Technologies

Tai-Ying Cheng, University of Oxford

Sheikh Khalid, Sensat

Bo Yang, The Hong Kong Polytechnic University

Ronald Clark, Imperial College London

Yulan Guo, National University of Defense Technology

Ales Leonardis, University of Birmingham

Niki Trigoni ,University of Oxford

Andrew Markham, University of Oxford

• 05/12/2022: Competition starts
• 10/10/2022: Competition ends
• 10/15/2022：Decision to Participants
• 10/25/2022：Workshop（Half-day）

Please contact Qingyong Hu if you have any questions.