  High Definition (HD) maps play an important role in modern traffic scenes.
However, the development of HD maps coverage grows slowly because of the cost
limitation. To efficiently model HD maps, we proposed a convolutional neural
network with a novel prediction layer and a zoom module, called LineNet. It is
designed for state-of-the-art lane detection in an unordered crowdsourced image
dataset. And we introduced TTLane, a dataset for efficient lane detection in
urban road modeling applications. Combining LineNet and TTLane, we proposed a
pipeline to model HD maps with crowdsourced data for the first time. And the
maps can be constructed precisely even with inaccurate crowdsourced data.
