  Inferring road graphs from satellite imagery is a challenging computer vision
task. Prior solutions fall into two categories: (1) pixel-wise
segmentation-based approaches, which predict whether each pixel is on a road,
and (2) graph-based approaches, which predict the road graph iteratively. We
find that these two approaches have complementary strengths while suffering
from their own inherent limitations.
  In this paper, we propose a new method, Sat2Graph, which combines the
advantages of the two prior categories into a unified framework. The key idea
in Sat2Graph is a novel encoding scheme, graph-tensor encoding (GTE), which
encodes the road graph into a tensor representation. GTE makes it possible to
train a simple, non-recurrent, supervised model to predict a rich set of
features that capture the graph structure directly from an image. We evaluate
Sat2Graph using two large datasets. We find that Sat2Graph surpasses prior
methods on two widely used metrics, TOPO and APLS. Furthermore, whereas prior
work only infers planar road graphs, our approach is capable of inferring
stacked roads (e.g., overpasses), and does so robustly.
