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 pap...

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Bibliographic Details
Published in:arXiv.org 2024-06
Main Authors: Duan, Liuyun, Mapurisa, Willard, Leras, Maxime, Lotter, Leigh, Tarabalka, Yuliya
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Digital mapping
Image resolution
Machine learning
Network topologies
Roads
Satellite imagery
Strings
Online Access:Get full text
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Summary: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.
ISSN:2331-8422