  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 leverage recent open source advances and the high quality
SpaceNet dataset to explore road network extraction at scale, an approach we
call City-scale Road Extraction from Satellite Imagery (CRESI). Specifically,
we create an algorithm to extract road networks directly from imagery over
city-scale regions, which can subsequently be used for routing purposes. We
quantify the performance of our algorithm with the APLS and TOPO
graph-theoretic metrics over a diverse 608 square kilometer test area covering
four cities. We find an aggregate score of APLS = 0.73, and a TOPO score of
0.58 (a significant improvement over existing methods). Inference speed is 160
square kilometers per hour on modest hardware. Finally, we demonstrate that one
can use the extracted road network for any number of applications, such as
optimized routing.
