  Autonomous driving car is becoming more of a reality, as a key
component,high-definition(HD) maps shows its value in both market place and
industry. Even though HD maps generation from LiDAR or stereo/perspective
imagery has achieved impressive success, its inherent defects cannot be
ignored. In this paper, we proposal a novel method for Highway HD maps modeling
using pixel-wise segmentation on satellite imagery and formalized hypotheses
linking, which is cheaper and faster than current HD maps modeling approaches
from LiDAR point cloud and perspective view imagery, and let it becomes an
ideal complementary of state of the art. We also manual code/label an HD road
model dataset as ground truth, aligned with Bing tile image server, to train,
test and evaluate our methodology. This dataset will be publish at same time to
contribute research in HD maps modeling from aerial imagery.
