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
difficult 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.

通过采用基于卷积神经网络（CNN）的决策函数来引导迭代搜索过程，RoadTracer 可以从高分辨率航空图像中自动构建道路网络图，并在误差率为 5％时，相较于分割方法，可以准确地捕捉到多达 45％更多的交叉点。