The modern road network topology comprises intricately designed structures that introduce complexity when automatically reconstructing road networks. While open resources like OpenStreetMap (OSM) offer road networks with well-defined topology, they may not always be up to date worldwide. In this paper, we propose a fully automated pipeline for extracting road networks from very-high-resolution (VHR) satellite imagery. Our approach directly generates road line-strings that are seamlessly connected and precisely positioned. The process involves three key modules: a CNN-based neural network for road segmentation, a graph optimization algorithm to convert road predictions into vector line-strings, and a machine learning model for classifying road materials. Compared to OSM data, our results demonstrate significant potential for providing the latest road layouts and precise positions of road segments.
@article{arxiv.2406.14941,
title = {Brightearth roads: Towards fully automatic road network extraction from satellite imagery},
author = {Liuyun Duan and Willard Mapurisa and Maxime Leras and Leigh Lotter and Yuliya Tarabalka},
journal= {arXiv preprint arXiv:2406.14941},
year = {2024}
}