Geometrically modeling 2D scattered points: a review of the potential for methodologically improving mobile laser scanning in data processing

Lin, Yi; Hyyppä, Juha

doi:10.1080/17538947.2013.781239

Cited by 3 publications

(1 citation statement)

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“…However, MLS has the challenges of large data processing. Few analysis methods have been tailored for processing massive MLS point clouds while taking full advantage of their high point density [153]. The MLS data provides more profile information along the scanning paths, while ALS provides vertically earth surface representation.…”

Section: Data Processing Framework Tailored To Mlsmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Data Processing Framework Tailored To Mlsmentioning

confidence: 99%