  Deep learning is revolutionizing the mapping industry. Under lightweight
human curation, computer has generated almost half of the roads in Thailand on
OpenStreetMap (OSM) using high-resolution aerial imagery. Bing maps are
displaying 125 million computer-generated building polygons in the U.S. While
tremendously more efficient than manual mapping, one cannot map out everything
from the air. Especially for roads, a small prediction gap by image occlusion
renders the entire road useless for routing. Misconnections can be more
dangerous. Therefore computer-based mapping often requires local verifications,
which is still labor intensive. In this paper, we propose to leverage
crowdsourced GPS data to improve and support road extraction from aerial
imagery. Through novel data augmentation, GPS rendering, and 1D transpose
convolution techniques, we show almost 5% improvements over previous
competition winning models, and much better robustness when predicting new
areas without any new training data or domain adaptation.
