Leveraging Crowdsourced GPS Data for Road Extraction from Aerial Imagery
This work addresses the challenge of mapping roads efficiently for the mapping industry, though it is incremental as it builds on existing methods with new data integration.
The paper tackles the problem of road extraction from aerial imagery by leveraging crowdsourced GPS data to improve accuracy and robustness, achieving almost 5% improvements over previous competition-winning models.
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.