City Scapes – Depth and Segmentation

City Scapes – Depth and Segmentation

Datasets

City Scapes – Depth and Segmentation

File

City Scapes – Depth and Segmentation

Use Case

Computer Vision

Description

This dataset is a preprocessed dataset of the City Scapes dataset, to be used for two tasks: Depth Estimation and Semantic Segmentation.

About Dataset

Context

This dataset is a preprocessed version of the Cityscapes dataset, specifically designed for two tasks: Depth Estimation and Semantic Segmentation. Firstly, it has been tailored to facilitate accurate Depth Estimation. Secondly, it supports Semantic Segmentation tasks effectively.

Ultimately, this preprocessed dataset aims to enhance the usability of the Cityscapes data for these specific purposes.

Content

The dataset contains 128 x 256 sized images, their 19 class semantic segmentation labels and inverse depth labels.

Acknowledgements

The original dataset is taken from this website and the preprocessed ones are taken from this website.

License

The Cityscapes dataset, available from cityscapes-dataset.com, is provided freely to both academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. However, permission to use the data comes with certain conditions.

Firstly, it is important to note that the dataset is provided “AS IS,” without any express or implied warranty. Although every effort has been made to ensure its accuracy, Daimler AG, MPI Informatics, and TU Darmstadt do not accept any responsibility for errors or omissions.

In addition, it is required that you include a reference to the Cityscapes Dataset in any work that makes use of it. For research papers, you should cite the preferred publication listed on the Cityscapes website. Similarly, for other media, you should either cite the preferred publication or link to the Cityscapes website.

Moreover, you are not permitted to distribute this dataset or any modified versions. Nevertheless, it is permissible to distribute derivative works, as long as they are abstract representations of the dataset, such as models trained on it or additional annotations that do not directly include any of the original data. These derivative works must not allow for the recovery of the dataset or anything similar in character.

Furthermore, the dataset and any derivative works may not be used for commercial purposes. For example, you cannot license or sell the data or use it with the intent of procuring a commercial gain.

Ultimately, all rights not expressly granted to you are reserved by Daimler AG, MPI Informatics, and TU Darmstadt. In summary, while the Cityscapes dataset is available for various non-commercial uses, it is crucial to adhere to the specified conditions and acknowledge the original source appropriately.

Citations

M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The Cityscapes Dataset for Semantic Urban Scene Understanding,” in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
Liu, Shikun and Johns, Edward and Davison, Andrew J, “End-to-End Multi-task Learning with Attention” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

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