City-scale Road Extraction from Satellite Imagery
This work addresses the challenge of extracting road networks for applications like routing, representing an incremental improvement over existing methods.
The paper tackled automated road network extraction from satellite imagery at a city scale, achieving an APLS score of 0.73 and a TOPO score of 0.58, with inference speeds of 160 square kilometers per hour on modest hardware.
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.