Road extraction in remote sensing data: A survey
Road extraction in remote sensing data: A survey
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DOI：
10.1016/j.jag.2022.102833
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发表时间：
2022-08-01
影响因子：
7.5
通讯作者：
Li, Deren
中科院分区：
文献类型：
--
作者：
Chen, Ziyi;Deng, Liai;Li, Deren
从遥感数据中自动提取道路有各种用途，从智能城市、智能交通、城市规划、自动驾驶到应急管理的数字孪生。许多研究都集中在推动航空和卫星光学图像、合成孔径雷达（SAR）图像和激光雷达点云自动道路提取方法的进展上。在过去的10年里，在文献中找不到对这一主题更全面的调查。本文试图对利用二维地球观测图像和三维激光雷达点云的道路提取方法进行综合综述。在这篇综述中，我们首先提出了一个树状结构，将文献分为二维和三维。然后，进一步的方法级别分类演示了在二维和三维。在二维和三维方面，我们介绍和分析了近十年来发表的文献。除了方法之外，我们还回顾了常用数据的各个方面。最后，本文探讨了现有的挑战和未来的发展趋势。
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.