The semantic segmentation GT for road surfaces contains 701 frames from RTK dataset. Classes are defined as follows:
- Background, everything being unrelated to the road surface;
- Asphalt, roads with asphalt surface;
- Paved, different pavements (eg.: Cobblestone);
- Unpaved, for unpaved roads;
- Markings, to the road markings;
- Speed-Bump, for the speed-bumps on the road;
- Cats-Eye, for the cats-eye found on the road, both on the side and in the center of the path;
- Storm-Drain, usually at the side edges of the road;
- Patch, for the various patches found on asphalt road;
- Water-Puddle, we use this class also for muddy regions;
- Pothole, for different types and sizes of potholes, no matter if they are on asphalt, paved or unpaved roads;
- Cracks, used in different road damages, like ruptures.
@misc{rateke:2020.3,
title = {Road surface detection and differentiation considering surface damages},
author = {Thiago Rateke and Aldo von Wangenheim},
journal={Autonomous Robots},
year={2021},
month={Jan},
day={11},
issn={1573-7527},
doi={10.1007/s10514-020-09964-3},
url={https://doi.org/10.1007/s10514-020-09964-3}
}