  Detecting lane lines from sensors is becoming an increasingly significant
part of autonomous driving systems. However, less development has been made on
high-definition lane-level mapping based on aerial images, which could
automatically build and update offline maps for auto-driving systems. To this
end, our work focuses on extracting fine-level detailed lane lines together
with their topological structures. This task is challenging since it requires
large amounts of data covering different lane types, terrain and regions. In
this paper, we introduce for the first time a large-scale aerial image dataset
built for lane detection, with high-quality polyline lane annotations on
high-resolution images of around 80 kilometers of road. Moreover, we developed
a baseline deep learning lane detection method from aerial images, called
AerialLaneNet, consisting of two stages. The first stage is to produce
coarse-grained results at point level, and the second stage exploits the
coarse-grained results and feature to perform the vertex-matching task,
producing fine-grained lanes with topology. The experiments show our approach
achieves significant improvement compared with the state-of-the-art methods on
our new dataset. Our code and new dataset are available at
https://github.com/Jiawei-Yao0812/AerialLaneNet.
