Planetscapes
High-quality computer vision dataset for semantic segmentation and scene understanding
High-quality computer vision dataset for semantic segmentation and scene understanding
Get an overview of the Planetscapes Dataset, including its main features, annotation policies, and the definitions of the semantic categories it contains.
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View some sample images to get a deeper understanding of the types and quality of annotations, as well as the metadata provided.
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Learn about the challenges in the benchmark suites, the corresponding evaluation metrics, and the performance results of evaluation methods.
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We present a new large-scale dataset that contains a diverse set of
stereo video sequences recorded in street scenes from 50 different
cities, with high quality pixel-level annotations of 5 000 frames in
addition to a larger set of 20 000 weakly annotated frames. The
dataset is thus an order of magnitude larger than similar previous
attempts. Details on annotated classes and examples of our
annotations are available at this webpage.
The Planetscapes Dataset is intended for:
1. assessing the performance of vision algorithms for major tasks of
semantic urban scene understanding: pixel-level, instance-level, and
panoptic semantic labeling;
2. supporting research that aims to exploit large volumes of
(weakly) annotated data, e.g. for training deep neural networks.
This Planetscapes Dataset is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.