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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (1) : 66-74     DOI: 10.6046/gtzyyg.2020.01.10
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Automated extraction of roads from mobile laser scanning point clouds by image semantic segmentation
Bo YU1, Junjun ZHANG2, Chungeng LI1(), Jubai AN1
1. School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
2. Beijing Dilu Technology Co., Ltd., Beijing 100193, China
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Abstract  

Mobile laser scanning can acquire lots of dense point clouds. Therefore, how to get high-quality road point clouds is a problem worthy of further study. This paper proposes a method for automatic extraction of roads from mobile laser scanning point clouds by image semantic segmentation. The authors use a four-step strategy: First, semantic segmentation images are created using 2D panoramic images. Then, fusion and matching are conducted to get rough classification results. After that, the 3D Hough transform is used to get the segmentation plane before fitting. Finally, a finely classified point cloud is obtained through local optimization operations. The authors extracted and evaluated two different points of cloud data on urban roads. The accuracy and integrity are all over 99%. The extraction quality is high enough to adapt the application requirements in practice. The method proposed by the authors can extract road point clouds in different situations and has less primitive constraints on point cloud data. It shows a significant improvement in both universality and robustness compared with other methods.

Keywords road extraction      semantic segmentation      deep learning      fusion and matching      Hough transform     
:  P208  
  TP183  
Corresponding Authors: Chungeng LI     E-mail: lichungeng@dlmu.edu.cn
Issue Date: 14 March 2020
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Bo YU
Junjun ZHANG
Chungeng LI
Jubai AN
Cite this article:   
Bo YU,Junjun ZHANG,Chungeng LI, et al. Automated extraction of roads from mobile laser scanning point clouds by image semantic segmentation[J]. Remote Sensing for Land & Resources, 2020, 32(1): 66-74.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.01.10     OR     https://www.gtzyyg.com/EN/Y2020/V32/I1/66
Fig.1  Flow chart of road point cloud extraction
Fig.2  Schematic diagram of semantic segmentation network structure
Fig.3  Comparison of two convolution methods
Fig.4  Atrous convolution diagram
Fig.5  Structure diagram of atrous spatial pyramid pooling
Fig.6  Schematic diagram of spherical coordinate transformation
Fig.7  3D Hough transform diagram
Fig.8  Plane Hough space
Fig.9  Growth algorithm diagram
Fig.10  Panoramic image and segmentation
Fig.11  Fusion registration of point cloud and image
Fig.12  Overall point cloud classification
Fig.13  Road point cloud classification
Fig.14  The second road point cloud classification
数据 p t q
第一段 99.93 99.88 99.81
第二段 99.78 99.52 99.31
Tab.1  Point cloud data accuracy evaluation(%)
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