Cityscapes

The Cityscapes Dataset for Semantic Urban Scene Understanding. Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations; 20000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark.


References in zbMATH (referenced in 19 articles , 1 standard article )

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  1. Barrowclough, Oliver J. D.; Muntingh, Georg; Nainamalai, Varatharajan; Stangeby, Ivar: Binary segmentation of medical images using implicit spline representations and deep learning (2021)
  2. Galanti, Tomer; Benaim, Sagie; Wolf, Lior: Risk bounds for unsupervised cross-domain mapping with IPMs (2021)
  3. Li, Haoliang; Wan, Renjie; Wang, Shiqi; Kot, Alex C.: Unsupervised domain adaptation in the wild via disentangling representation learning (2021)
  4. Marcos Nieto, Orti Senderos, Oihana Otaegui: Boosting AI applications: Labeling format for complex datasets (2021) not zbMATH
  5. Suchan, Jakob; Bhatt, Mehul; Varadarajan, Srikrishna: Commonsense visual sensemaking for autonomous driving -- on generalised neurosymbolic online abduction integrating vision and semantics (2021)
  6. Wang, Zhengyang; Ji, Shuiwang: Smoothed dilated convolutions for improved dense prediction (2021)
  7. Yuan, Yuhui; Huang, Lang; Guo, Jianyuan; Zhang, Chao; Chen, Xilin; Wang, Jingdong: OCNet: object context for semantic segmentation (2021)
  8. Hehn, Thomas M.; Kooij, Julian F. P.; Hamprecht, Fred A.: End-to-end learning of decision trees and forests (2020)
  9. Li, Ke; Peng, Shichong; Zhang, Tianhao; Malik, Jitendra: Multimodal image synthesis with conditional implicit maximum likelihood estimation (2020)
  10. Samsonov, N. A.; Gneushev, A. N.; Matveev, I. A.: Training a classifier by descriptors in the space of the Radon transform (2020)
  11. Shao, Wenqi; Li, Jingyu; Ren, Jiamin; Zhang, Ruimao; Wang, Xiaogang; Luo, Ping: SSN: learning sparse switchable normalization via SparsestMax (2020)
  12. Song, Taeyong; Kim, Youngjung; Oh, Changjae; Jang, Hyunsung; Ha, Namkoo; Sohn, Kwanghoon: Simultaneous deep stereo matching and dehazing with feature attention (2020)
  13. Valada, Abhinav; Mohan, Rohit; Burgard, Wolfram: Self-supervised model adaptation for multimodal semantic segmentation (2020)
  14. Wang, Yong; Zhang, Dongfang; Dai, Guangming: Classification of high resolution satellite images using improved U-Net (2020)
  15. Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, Juergen Gall: SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences (2019) arXiv
  16. Patil A., Malla S., Gang H., Chen Y.-T.: The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes (2019) arXiv
  17. Mauch, Lukas; Wang, Chunlai; Yang, Bin: Subset selection for visualization of relevant image fractions for deep learning based semantic image segmentation (2018)
  18. Xinyu Huang, Peng Wang, Xinjing Cheng, Dingfu Zhou, Qichuan Geng, Ruigang Yang: The ApolloScape Open Dataset for Autonomous Driving and its Application (2018) arXiv
  19. Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, Bernt Schiele: The Cityscapes Dataset for Semantic Urban Scene Understanding (2016) arXiv