  Road-boundary detection is important for autonomous driving. For example, it
can be used to constrain vehicles running on road areas, which ensures driving
safety. Compared with on-line road-boundary detection using on-vehicle
cameras/Lidars, off-line detection using aerial images could alleviate the
severe occlusion issue. Moreover, the off-line detection results can be
directly used to annotate high-definition (HD) maps. In recent years,
deep-learning technologies have been used in off-line detection. But there is
still lacking a publicly available dataset for this task, which hinders the
research progress in this area. So in this paper, we propose a new benchmark
dataset, named \textit{Topo-boundary}, for off-line topological road-boundary
detection. The dataset contains 21,556 $1000\times1000$-sized 4-channel aerial
images. Each image is provided with 8 training labels for different sub-tasks.
We also design a new entropy-based metric for connectivity evaluation, which
could better handle noises or outliers. We implement and evaluate 3
segmentation-based baselines and 5 graph-based baselines using the dataset. We
also propose a new imitation-learning-based baseline which is enhanced from our
previous work. The superiority of our enhancement is demonstrated from the
comparison. The dataset and our-implemented codes for the baselines are
available at https://sites.google.com/view/topo-boundary.
