Cityscapes Dataset

The Cityscapes Dataset focuses on semantic understanding of urban street scenes. In the following, we give an overview on the design choices that were made to target the dataset’s focus.


Type of annotations

  • Semantic
  • Instance-wise
  • Dense pixel annotations


  • 30 classes


  • 50 cities
  • Several months (spring, summer, fall)
  • Daytime
  • Good/medium weather conditions
  • Manually selected frames
    • Large number of dynamic objects
    • Varying scene layout
    • Varying background


  • 5 000 annotated images with fine annotations (examples)
  • 20 000 annotated images with coarse annotations (examples)


  • Preceding and trailing video frames. Each annotated image is the 20th image from a 30 frame video snippets (1.8s)
  • Corresponding right stereo views
  • GPS coordinates
  • Ego-motion data from vehicle odometry
  • Outside temperature from vehicle sensor

Benchmark suite and evaluation server

  • Pixel-level semantic labeling
  • Instance-level semantic labeling

Company Type: Academia

Region: Europe

Industry Category: Urban Development Infrastructure Scene Recognition

Data set: Semantic Understanding of Urban Street Scenes