Brightearth roads: Towards fully automatic road network extraction from satellite imagery
<|reference_start|>Brightearth roads: Towards fully automatic road network extraction from satellite imagery: 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.<|reference_end|>
@article{duan2024brightearth,
    title={Brightearth roads: Towards fully automatic road network extraction from
  satellite imagery},
    author={Liuyun Duan (LCT), Willard Mapurisa (LCT), Maxime Leras (LCT), Leigh
  Lotter (LCT), Yuliya Tarabalka (LCT)},
    journal={IGARSS2024, Jul 2024, ATHENE, Greece},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14941},
    primaryClass={cs.CV}
}