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
- 30 classes
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
Company Type: Academia
Region: Europe
Industry Category: Urban Development Infrastructure Scene Recognition
Data set: Semantic Understanding of Urban Street Scenes
