  High-Definition (HD) maps can provide precise geometric and semantic
information of static traffic environments for autonomous driving.
Road-boundary is one of the most important information contained in HD maps
since it distinguishes between road areas and off-road areas, which can guide
vehicles to drive within road areas. But it is labor-intensive to annotate road
boundaries for HD maps at the city scale. To enable automatic HD map
annotation, current work uses semantic segmentation or iterative graph growing
for road-boundary detection. However, the former could not ensure topological
correctness since it works at the pixel level, while the latter suffers from
inefficiency and drifting issues. To provide a solution to the aforementioned
problems, in this letter, we propose a novel system termed csBoundary to
automatically detect road boundaries at the city scale for HD map annotation.
Our network takes as input an aerial image patch, and directly infers the
continuous road-boundary graph (i.e., vertices and edges) from this image. To
generate the city-scale road-boundary graph, we stitch the obtained graphs from
all the image patches. Our csBoundary is evaluated and compared on a public
benchmark dataset. The results demonstrate our superiority. The accompanied
demonstration video is available at our project page
\url{https://sites.google.com/view/csboundary/}.

Computer Vision and Pattern Recognition;Robotics