The Cityscapes dataset is introduced as a comprehensive benchmark suite for semantic urban scene understanding, addressing the lack of large-scale, diverse datasets in this field. The dataset includes stereo video sequences from 50 cities, with 5000 images annotated at the pixel level and 20000 images with coarse annotations. It aims to capture the complexity and variability of real-world urban scenes, providing depth information through stereo vision. The dataset is designed to facilitate research on 3D scene understanding and includes a detailed analysis of its characteristics and performance evaluation of state-of-the-art approaches. The paper also discusses the challenges and opportunities presented by the dataset, emphasizing the need for methods that can handle scale variation and small objects in urban scenes.The Cityscapes dataset is introduced as a comprehensive benchmark suite for semantic urban scene understanding, addressing the lack of large-scale, diverse datasets in this field. The dataset includes stereo video sequences from 50 cities, with 5000 images annotated at the pixel level and 20000 images with coarse annotations. It aims to capture the complexity and variability of real-world urban scenes, providing depth information through stereo vision. The dataset is designed to facilitate research on 3D scene understanding and includes a detailed analysis of its characteristics and performance evaluation of state-of-the-art approaches. The paper also discusses the challenges and opportunities presented by the dataset, emphasizing the need for methods that can handle scale variation and small objects in urban scenes.