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

arXiv: https://arxiv.org/abs/1908.09715 · HTML: https://mlsanity.com/paper/1908.09715/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2019.

## Built on by (most cited first, 12 of 12)

- [RNGDet: Road Network Graph Detection by Transformer in Aerial Images](https://mlsanity.com/paper/2202.07824/) (2022)
- [RNGDet++: Road Network Graph Detection by Transformer with Instance Segmentation and Multi-scale Features Enhancement](https://mlsanity.com/paper/2209.10150/) (2022)
- [csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps](https://mlsanity.com/paper/2111.06020/) (2021)
- [Road Extraction with Satellite Images and Partial Road Maps](https://mlsanity.com/paper/2303.12394/) (2023)
- [Towards Global-Scale Crowd+AI Techniques to Map and Assess Sidewalks for People with Disabilities](https://mlsanity.com/paper/2206.13677/) (2022)
- [A Concise Survey on Lane Topology Reasoning for HD Mapping](https://mlsanity.com/paper/2504.01989/) (2025)
- [Leveraging Topology for Domain Adaptive Road Segmentation in Satellite and Aerial Imagery](https://mlsanity.com/paper/2309.15625/) (2023)
- [Vehicle Vectors and Traffic Patterns from Planet Imagery](https://mlsanity.com/paper/2406.06320/) (2024)
- [Primitive Graph Learning for Unified Vector Mapping](https://mlsanity.com/paper/2206.13963/) (2022)
- [MultiScale Probability Map guided Index Pooling with Attention-based learning for Road and Building Segmentation](https://mlsanity.com/paper/2302.09411/) (2023)
- [AI Powered Road Network Prediction with Multi-Modal Data](https://mlsanity.com/paper/2312.17040/) (2023)
- [Flood Data Analysis on SpaceNet 8 Using Apache Sedona](https://mlsanity.com/paper/2404.18235/) (2024)

## Abstract (arXiv)

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.