Dataset Overview


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.

 

Features

Type of annotations

  • Semantic
  • Instance-wise
  • Dense pixel annotations

Complexity

Diversity

  • 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

Volume

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

Metadata

  • 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

Type of annotations

Annotation example

Contained cities

 

Labeling Policy

Labeled foreground objects must never have holes, i.e. if there is some background visible 'through' some foreground object, it is considered to be part of the foreground. This also applies to regions that are highly mixed with two or more classes: they are labeled with the foreground class. Examples: tree leaves in front of house or sky (everything tree), transparent car windows (everything car).

 

Class Definitions

Please click on the individual classes for details on their definitions.

GroupClasses
flatroad · sidewalk · parking+ · rail track+
humanperson* · rider*
vehiclecar* · truck* · bus* · on rails* · motorcycle* · bicycle* · caravan*+ · trailer*+
constructionbuilding · wall · fence · guard rail+ · bridge+ · tunnel+
objectpole · pole group+ · traffic sign · traffic light
naturevegetation · terrain
skysky
voidground+ · dynamic+ · static+
  • * Single instance annotations are available. However, if the boundary between such instances cannot be clearly seen, the whole crowd/group is labeled together and annotated as group, e.g. car group.
  • + This label is not included in any evaluation and treated as void (or in the case of license plate as the vehicle mounted on).