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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.14941 |
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| _version_ | 1866929393856675840 |
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| author | Duan, Liuyun Mapurisa, Willard Leras, Maxime Lotter, Leigh Tarabalka, Yuliya |
| author_facet | Duan, Liuyun Mapurisa, Willard Leras, Maxime Lotter, Leigh Tarabalka, Yuliya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_14941 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Brightearth roads: Towards fully automatic road network extraction from satellite imagery Duan, Liuyun Mapurisa, Willard Leras, Maxime Lotter, Leigh Tarabalka, Yuliya Computer Vision and Pattern Recognition 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. |
| title | Brightearth roads: Towards fully automatic road network extraction from satellite imagery |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.14941 |