Brightearth roads: Towards fully automatic road network extraction from satellite imagery
This addresses the need for current and accurate road network data globally, particularly where existing maps are outdated, but it is incremental as it builds on existing segmentation and optimization techniques.
The authors tackled the problem of automatically reconstructing road networks from satellite imagery, proposing a fully automated pipeline that generates connected and precisely positioned road line-strings, with results showing significant potential for providing up-to-date road layouts compared to OpenStreetMap.
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.