Generating community road network from GPS trajectories via style transfer.

Road network generation from massive trajectories has mainly focused on the mining of urban arterial roads while the demand for refined road networks in communities full of low-grade roads is increasing. Trajectory GPS signals in communities tend to be very noisy and hard to label for general modeling. We propose Style-Transfer-Roadnet-Generation(STRG), a method based on un-supervised image style transfer to discover roads from trajectories in these communities. First, we convert the trajectory data into raster images. Then we train a style transferer to transform the raster images into road network style images. At last, the road networks are recovered and refined from predicted images. Experiments on the large-scale data and online deployment results show that STRG can effectively model the texture style of road networks in communities and discover new road networks from trajectories in a low-cost manner, outperforming strong baseline approaches.