Example images from the TAS500 dataset. The TAS500
dataset contains fine-grained terrain and vegetation annotations of over 500 scenes in unstructured environments.
Research in autonomous driving for unstructured
environments suffers from a lack of semantically labeled datasets
compared to its urban counterpart. Urban and unstructured
outdoor environments are challenging due to the varying lighting
and weather conditions during a day and across seasons. In
this paper, we introduce TAS500, a novel semantic segmentation
dataset for autonomous driving in unstructured environments.
TAS500 offers fine-grained vegetation and terrain classes to
learn drivable surfaces and natural obstacles in outdoor scenes
effectively. We evaluate the performance of modern semantic
segmentation models with an additional focus on their efficiency.
Our experiments demonstrate the advantages of fine-grained
semantic classes to improve the overall prediction accuracy,
especially along the class boundaries.
2021-01-10: Initial release of the TAS500v1.0 dataset as part of the ICPR publication
2021-04-15: Release of the TAS500v1.1 dataset. In the updated dataset we polished the semantic maps and cropped all driving images to the fixed image size of 620px x 2026px. Download the TAS500v1.1 dataset here.
Class distribution in the TAS500 dataset. Number of fine-grained pixels (y-axis) per class and their associated category (x-axis).
Data collection pipeline. The data was collected using the autonomous vehicle
MuCAR-3. We recorded data with a frame rate of 10 Hz and cut off most of the sky
and ego vehicle hood from all images. The final images have a
resolution of 620px x 2026px. Our label rate amounts to around
0.1 Hz, and we consequently provide a pixel-wise semantic
mask for every hundredth recorded image.
The TAS500 dataset is copyrighted and published under the Attribution-NonCommercial-ShareAlike 3.0 license.
This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license.
Send us an email and we will get back to you with a download link.
The authors gratefully acknowledge funding by the Federal Office of
Bundeswehr Equipment, Information Technology and In-Service Support
(BAAINBw). This webpage template is based on the Factored3D project page.