  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.
