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Title	Road Extraction In Remote Sensing Data: A Survey
ID_Doc	46729
Authors	Chen Z.; Deng L.; Luo Y.; Li D.; Marcato Junior J.; Nunes Gonçalves W.; Awal Md Nurunnabi A.; Li J.; Wang C.; Li D.
Year	2022
Published	International Journal of Applied Earth Observation and Geoinformation, 112
DOI	http://dx.doi.org/10.1016/j.jag.2022.102833
Abstract	Automated extraction of roads from remotely sensed data come forth various usages ranging from digital twins for smart cities, intelligent transportation, urban planning, autonomous driving, to emergency management. Many studies have focused on promoting the progress of methods for automated road extraction from aerial and satellite optical images, synthetic aperture radar (SAR) images, and LiDAR point clouds. In the past 10 years, no a more comprehensive survey on this topic could be found in literature. This paper attempts to provide a comprehensive survey on road extraction methods that use 2D earth observing images and 3D LiDAR point clouds. In this review, we first present a tree-structure that separate the literature into 2D and 3D. Then, further methodologies level classification is demonstrated both in 2D and 3D. In 2D and 3D, we introduce and analyze the literature published in the last ten years. Except for the methodologies, we also review the aspects of data commonly used. Finally, this paper explores the existing challenges and future trends. © 2022
Author Keywords	2D and 3D; Point clouds; Remote sensing; Review; Road extraction

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