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, not just the shortest geographic distance. We compare SpaceNet labels to OpenStreetMap (OSM) labels, and find that models both trained and tested on SpaceNet labels outperform OSM labels by 60 or greater. For a diverse test set of SpaceNet data and a traditional edge weight of geometric distance, we find an aggregate of 5 improvement over existing methods. 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.
@cite_30: 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, and 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.
We build upon CRESI v1 @cite_30 that scaled up narrow-field road network extraction methods; in this work we focus primarily on developing methodologies to infer road speeds and travel times, but also improve algorithmic performance and inference speed.