City-Scale Road Extraction from Satellite Imagery v2: Road Speeds and Travel Times

TL;DR

This paper introduces CRESIv2, a scalable method for extracting detailed road networks from satellite imagery, including speed limits and travel times, enabling more accurate routing than traditional distance-based methods.

Contribution

CRESIv2 is the first approach to jointly extract road networks with semantic features and travel times at city scale from satellite imagery, improving routing accuracy.

Findings

Models trained on SpaceNet outperform OpenStreetMap labels by over 60%.

Our method improves map topology and path similarity metrics by 5-23%.

Travel time-based edge weights maintain high accuracy with only 4% metric score decrease.

Abstract

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.