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Semantic understanding of urban street scenes through visual perception has been widely studied due to many possible practical applications. Key challenges arise from the high visual complexity of such scenes. In this paper, we present ongoing work on a new large-scale dataset for (1) assessing the performance of vision al-gorithms for different tasks of semantic urban scene understand-ing, including scene labeling, instance-level scene labeling, and object detection; (2) supporting research that aims to exploit large volumes of (weakly) annotated data, e.g. for training deep neural networks. We aim to provide a large and diverse set of stereo video sequences recorded in street scenes from 50 different cities, with high quality pixel-level annotations of 5000 frames in addition to a larger set of weakly annotated frames. The dataset is thus an order of magnitude larger than similar previous attempts. Several aspects are still up for discussion, and timely feed-back from the community would be greatly appreciated. Details on annotated classes and examples will be available at www. cityscapes-dataset.net. Moreover, we will use this web-site to collect remarks and suggestions. 1.