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arXiv · 2406.14941

Brightearth roads: Towards fully automatic road network extraction from satellite imagery

Liuyun Duan·Willard Mapurisa·Maxime Leras·Leigh Lotter·Yuliya Tarabalka
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Abstract

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.

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Liuyun Duan, Willard Mapurisa, Maxime Leras, Leigh Lotter, Yuliya Tarabalka. 2024-06-21. Brightearth roads: Towards fully automatic road network extraction from satellite imagery. https://arxiv.org/abs/2406.14941

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