Terrestrial Laser Scanners

Petrie, G.; Toth, Charles K.

doi:10.1201/9781315154381-2

Cited by 20 publications

(14 citation statements)

References 4 publications

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“…With the help of an algorithm, survey data is converted into 3D building model elements and building components in the native environment of the design software. The setup is based on the operational principles of terrestrial laser scanners (TLS) [4] With the help of a laser module, an "ultra-low-density" point cloud is produced by taking multiple point-to-point measurements on each surface or edge. This is then convertible into model elements that can be interpreted in architectural design software using a processing algorithm.…”

Section: Concept Principles and Build-upmentioning

confidence: 99%

Laser Survey Tool and Survey Methodology for Direct Architectural Cad Software Connection

Máder

Szilágyi

Rák

et al. 2020

Műszaki Tudományos Közlemények

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Building Information Modeling (BIM) has been the fastest growing methodology in architectural design, construction, preliminary works and in several other engineering activities in the past few years. It is mostly implemented in the fields of design, construction and building operation, however, there are still unexploited possibilities in further areas – such as building surveys. Many tools are available today to produce detailed and accurate 3D survey data, but specialists and custom software usually have to be involved in the process. Transforming this information into BIM models is also a time-consuming task, as their direct architectural design software integration is limited. The following article introduces a possible solution in order to improve communication and the modeling process.

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Section: Concept Principles and Build-upmentioning

confidence: 99%

Laser Survey Tool and Survey Methodology for Direct Architectural Cad Software Connection

Máder

Szilágyi

Rák

et al. 2020

Műszaki Tudományos Közlemények

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“…Technical aspects and eyesafety limit the amount wavelengths that can be used in commercial scanners due to high amount of energy carried by shorter wavelengths. Popular wavelengths are located in the near infrared (NIR) or shortwave infrared (SWIR) region, e.g., 905 nm, 1064 nm and 1550 nm (Toth and Petrie 2018). Other wavelengths that have been used include green laser (534 nm), red laser (690 nm) and NIR at 785 nm.…”

Section: Terrestrial Lidar Systems and Data Acquisitionmentioning

confidence: 99%

Utilizing multispectral lidar in the detection of declined trees

Junttila¹

2019

Diss. For.

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“…Platforms for LiDAR data collection include terrestrial laser scanners (TLS) (e.g., static LiDAR-mounted tripods), mobile laser scanners (MLS) (e.g., mobile mapping vehicles), and airborne laser scanners (ALS) (e.g., aircrafts and unpiloted aerial vehicles (UAV)), and to a lesser extent, orbital platforms [14][15][16]. Each collection platform has a unique associated scanner orientation, scan angle, point spacing, and laser footprint size, resulting in differences in their collected data [14][15][16][17][18][19][20]. ALS data typically exhibits an even point density distribution [19,21], a well-defined ground surface, a point spacing of roughly 0.5 m [22], and a laser footprint size of 10 cm-25 m [23].…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Ground-Point Classifiers for Terrestrial LiDAR

Roberts¹,

Lindsay²,

Berg³

2019

Remote Sensing

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Previous literature has compared the performance of existing ground point classification (GPC) techniques on airborne LiDAR (ALS) data (LiDAR—light detection and ranging); however, their performance when applied to terrestrial LiDAR (TLS) data has not yet been addressed. This research tested the classification accuracy of five openly-available GPC algorithms on seven TLS datasets: Zhang et al.’s inverted cloth simulation (CSF), Kraus and Pfeiffer’s hierarchical weighted robust interpolation classifier (HWRI), Axelsson’s progressive TIN densification filter (TIN), Evans and Hudak’s multiscale curvature classification (MCC), and Vosselman’s modified slope-based filter (MSBF). Classification performance was analyzed using the kappa index of agreement (KIA) and rasterized spatial distribution of classification accuracy datasets generated through comparisons with manually classified reference datasets. The results identified a decrease in classification accuracy for the CSF and HWRI classification of low vegetation, for the HWRI and MCC classifications of variably sloped terrain, for the HWRI and TIN classifications of low outlier points, and for the TIN and MSBF classifications of off-terrain (OT) points without any ground points beneath. Additionally, the results show that while no single algorithm was suitable for use on all datasets containing varying terrain characteristics and OT object types, in general, a mathematical-morphology/slope-based method outperformed other methods, reporting a kappa score of 0.902.

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Terrestrial Laser Scanners

Cited by 20 publications

References 4 publications

Laser Survey Tool and Survey Methodology for Direct Architectural Cad Software Connection

Laser Survey Tool and Survey Methodology for Direct Architectural Cad Software Connection

Utilizing multispectral lidar in the detection of declined trees

An Analysis of Ground-Point Classifiers for Terrestrial LiDAR

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