Automated road network extraction from remote sensing imagery remains a
significant challenge despite its importance in a broad array of applications.
To this end, we explore road network extraction at scale with inference of
semantic features of the graph, identifying speed limits and route travel times
for each roadway. We call this approach City-Scale Road Extraction from
Satellite Imagery v2 (CRESIv2), Including estimates for travel time permits
true optimal routing (rather than just the shortest geographic distance), which
is not possible with existing remote sensing imagery based methods. We evaluate
our method using two sources of labels (OpenStreetMap, and those from the
SpaceNet dataset), and find that models both trained and tested on SpaceNet
labels outperform OpenStreetMap labels by greater than 60%. We quantify the
performance of our algorithm with the Average Path Length Similarity (APLS) and
map topology (TOPO) graph-theoretic metrics over a diverse test area covering
four cities in the SpaceNet dataset. For a traditional edge weight of geometric
distance, we find an aggregate of 5% improvement over existing methods for
SpaceNet data. We also test our algorithm on Google satellite imagery with
OpenStreetMap labels, and find a 23% improvement over previous work. Metric
scores decrease by only 4% on large graphs when using travel time rather than
geometric distance for edge weights, indicating that optimizing routing for
travel time is feasible with this approach.

本文提出了一种基于 CRESIv2 方法的城市级别道路提取和路线计算方法，该方法包含语义特征识别以及道路速度限制和行车时间的计算，旨在优化路线计算而非简单的最短路径距离。实验证明，该方法在使用 SpaceNet 数据集的训练和测试数据时优于使用 OpenStreetMap 标签的方法 60% 以上，并通过度量平均路径长度相似度（APLS）和地图拓扑（TOPO）等指标对算法的性能进行了评估。