A Survey of Mobile Laser Scanning Applications and Key Techniques over Urban Areas

Wang Wang,Zhu Zhu,Liu Liu,Zheng Zheng,Li Li,Chen Chen

doi:10.3390/rs11131540

Abstract

Urban planning and management need accurate three-dimensional (3D) data such as light detection and ranging (LiDAR) point clouds. The mobile laser scanning (MLS) data, with up to millimeter-level accuracy and point density of a few thousand points/m2, have gained increasing attention in urban applications. Substantial research has been conducted in the past decade. This paper conducted a comprehensive survey of urban applications and key techniques based on MLS point clouds. We first introduce the key characteristics of MLS systems and the corresponding point clouds, and present the challenges and opportunities of using the data. Next, we summarize the current applications of using MLS over urban areas, including transportation infrastructure mapping, building information modeling, utility surveying and mapping, vegetation inventory, and autonomous vehicle driving. Then, we review common key issues for processing and analyzing MLS point clouds, including classification methods, object recognition, data registration, data fusion, and 3D city modeling. Finally, we discuss the future prospects for MLS technology and urban applications.

Highlights

Accurate three-dimensional (3D) point cloud data have been an important data source for 3D urban models, which are an integral part of urban planning, simulation, mapping and visualization, emergency response training, and so on [1]
Mobile laser scanning (MLS) data have been used in recent years in a wide range of urban applications, including urban land cover analysis [6,7,8,9], digital 3D city modeling [10,11], urban environment monitoring [12,13,14,15,16], and autonomous vehicle driving [17,18,19]
MLS is a crucial component for visual learning model, the average classification accuracy could be over 93.1%, while the number of projection images for rasterization had impact on the training and testing stages

Summary

Introduction

Accurate three-dimensional (3D) point cloud data have been an important data source for 3D urban models, which are an integral part of urban planning, simulation, mapping and visualization, emergency response training, and so on [1]. Due to the short measure range and flexibility of data acquisition, a MLS system can acquire very accurate (millimeter-level) point clouds of high point density (up to a few thousand points/m2) [3,4,5] Given those advantages, MLS data have been used in recent years in a wide range of urban applications, including urban land cover analysis [6,7,8,9], digital 3D city modeling [10,11], urban environment monitoring [12,13,14,15,16], and autonomous vehicle driving [17,18,19]. The use of these data has measure range and flexibility of data acquisition, a MLS system can acquire very accurate (millimeter-level) point clouds of high point density (up to a few thousand points/m2) [3,4,5]

Objectives

Methods

Findings

Conclusion