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6 juillet 2024
- handle: 10670/1.jy33sl
- hal: hal-04593369
http://creativecommons.org/licenses/by/ , info:eu-repo/semantics/OpenAccess
Mots-clés
Remote sensing deep learning semantic segmentation road network road materials Remote sensing deep learning semantic segmentation road network road materialsSujets proches
Position analysis Analysis situs Rubber-sheet geometry Remote terrain sensing Remote sensing systems Remote-sensing imagery Sensing, Remote Terrain sensing, Remote Learning process Tropes Speech, Figures of English language--Figures of speech Imagery Scholarship LearningCiter ce document
Liuyun Duan et al., « BRIGHTEARTH ROADS: TOWARDS FULLY AUTOMATIC ROAD NETWORK EXTRACTION FROM SATELLITE IMAGERY », HAL-SHS : géographie, ID : 10670/1.jy33sl
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Résumé
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.