Abstract
Mapping road networks is currently both expensive and
labor-intensive. High-resolution aerial imagery provides a
promising avenue to automatically infer a road network.
Prior work uses convolutional neural networks (CNNs) to
detect which pixels belong to a road (segmentation), and
then uses complex post-processing heuristics to infer graph
connectivity. We show that these segmentation methods
have high error rates because noisy CNN outputs are dif-
ficult to correct. We propose RoadTracer, a new method to
automatically construct accurate road network maps from
aerial images. RoadTracer uses an iterative search process guided by a CNN-based decision function to derive the
road network graph directly from the output of the CNN.
We compare our approach with a segmentation method on
fifteen cities, and find that at a 5% error rate, RoadTracer
correctly captures 45% more junctions across these cities