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.

本论文提出了一种新方法 Sat2Graph，将像素级分割和基于图形的方法的优势结合在一个框架中。通过一种新的编码方式，即图张量编码，Sat2Graph 能够从图像中直接捕捉图形结构的特征并预测道路图，并可以侦测不只平面的道路图，而能够稳健地预测堆叠道路（例如高架桥等）。实验显示，Sat2Graph 优于先前指标 TOPO 和 APLS 使用的其他方法。