Road extraction in remote sensing data: A survey

被引：112

作者：

Chen, Ziyi ^{[1
]}

Deng, Liai ^{[1
]}

Luo, Yuhua ^{[1
]}

Li, Dilong ^{[1
]}

Marcato Junior, Jose ^{[2
]}

Gonsalves, Wesley Nunes ^{[2
]}

Nurunnabi, Abdul Awal Md ^{[3
]}

Li, Jonathan ^{[4
]}

Wang, Cheng ^{[5
]}

Li, Deren ^{[6
]}

机构：

[1] Huaqiao Univ, Xiamen Key Lab Comp Vis & Pattern Recognit, Fujian Key Lab Big Data Intelligence & Secur, Dept Comp Sci &Technol, Jimei Rd 668, Xiamen, Fujian, Peoples R China

[2] Univ Fed Mato Grosso, Fac Engn, Architecture & Urbanism & Geog, Campo Grande, MS, Brazil

[3] Univ Luxembourg, Dept Geodesy & Geospatial Engn, L-1359 Luxembourg, Luxembourg

[4] Univ Waterloo, Dept Geog & Environm Management, Waterloo, ON N2L 3G1, Canada

[5] Xiamen Univ, Sch Informat, Xiamen 361005, Peoples R China

[6] Wuhan University, State Key Lab Informat Engn Surveying, Mapping & Remote Sensing, Wuhan 430079, Peoples R China

来源：

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION | 2022年 / 112卷

基金：

中国国家自然科学基金;

关键词：

Road extraction; Review; 2D and 3D; Remote sensing; Point clouds; LASER-SCANNING DATA; FULLY CONVOLUTIONAL NETWORK; CENTERLINE EXTRACTION; LIDAR DATA; LEARNING FRAMEWORK; SATELLITE IMAGES; AUTOMATED EXTRACTION; SHAPE-FEATURES; NEURAL-NETWORK; EDGE-DETECTION;

D O I：

10.1016/j.jag.2022.102833

中图分类号：

TP7 [遥感技术];

学科分类号：

081102 ; 0816 ; 081602 ; 083002 ; 1404 ;

摘要：

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.

引用

页数：24

共 246 条

[1]

Aishwarya L., 2020, P IEEE 11 INT C COMP, P1, DOI [10.1109/ICCCNT49239.2020.9225268, DOI 10.1109/ICCCNT49239.2020.9225268]

[2] Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks [J].