| Autor: |
Duan, Liuyun, Mapurisa, Willard, Leras, Maxime, Lotter, Leigh, Tarabalka, Yuliya |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IGARSS2024, Jul 2024, ATHENE, Greece |
| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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