  Road curb detection is important for autonomous driving. It can be used to
determine road boundaries to constrain vehicles on roads, so that potential
accidents could be avoided. Most of the current methods detect road curbs
online using vehicle-mounted sensors, such as cameras or 3-D Lidars. However,
these methods usually suffer from severe occlusion issues. Especially in
highly-dynamic traffic environments, most of the field of view is occupied by
dynamic objects. To alleviate this issue, we detect road curbs offline using
high-resolution aerial images in this paper. Moreover, the detected road curbs
can be used to create high-definition (HD) maps for autonomous vehicles.
Specifically, we first predict the pixel-wise segmentation map of road curbs,
and then conduct a series of post-processing steps to extract the graph
structure of road curbs. To tackle the disconnectivity issue in the
segmentation maps, we propose an innovative connectivity-preserving loss
(CP-loss) to improve the segmentation performance. The experimental results on
a public dataset demonstrate the effectiveness of our proposed loss function.
This paper is accompanied with a demonstration video and a supplementary
document, which are available at
\texttt{\url{https://sites.google.com/view/cp-loss}}.
