  Detection of road curbs is an essential capability for autonomous driving. It
can be used for autonomous vehicles to determine drivable areas on roads.
Usually, road curbs are detected on-line using vehicle-mounted sensors, such as
video cameras and 3-D Lidars. However, on-line detection using video cameras
may suffer from challenging illumination conditions, and Lidar-based approaches
may be difficult to detect far-away road curbs due to the sparsity issue of
point clouds. In recent years, aerial images are becoming more and more
worldwide available. We find that the visual appearances between road areas and
off-road areas are usually different in aerial images, so we propose a novel
solution to detect road curbs off-line using aerial images. The input to our
method is an aerial image, and the output is directly a graph (i.e., vertices
and edges) representing road curbs. To this end, we formulate the problem as an
imitation learning problem, and design a novel network and an innovative
training strategy to train an agent to iteratively find the road-curb graph.
The experimental results on a public dataset confirm the effectiveness and
superiority of our method. This work is accompanied with a demonstration video
and a supplementary document at https://tonyxuqaq.github.io/iCurb/.
