scholarly journals Detection of Water Leakage in Underground Tunnels Using Corrected Intensity Data and 3D Point Cloud of Terrestrial Laser Scanning

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 32471-32480 ◽  
Author(s):  
Teng Xu ◽  
Lijun Xu ◽  
Xiaolu Li ◽  
Junen Yao
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Intensity Data ◽  
3D Point Cloud ◽  
Water Leakage ◽  
Corrected Intensity

scholarly journals Revealing Changes in the Stem Form and Volume Allocation in Diverse Boreal Forests Using Two-Date Terrestrial Laser Scanning

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 835
Author(s):  
Ville Luoma ◽  
Tuomas Yrttimaa ◽  
Ville Kankare ◽  
Ninni Saarinen ◽  
Jiri Pyörälä ◽  
...  
Keyword(s):  
Tree Growth ◽  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Stem Volume ◽  
Accurate Information ◽  
Stem Form ◽  
Monitoring Period ◽  
Significant Difference

Tree growth is a multidimensional process that is affected by several factors. There is a continuous demand for improved information on tree growth and the ecological traits controlling it. This study aims at providing new approaches to improve ecological understanding of tree growth by the means of terrestrial laser scanning (TLS). Changes in tree stem form and stem volume allocation were investigated during a five-year monitoring period. In total, a selection of attributes from 736 trees from 37 sample plots representing different forest structures were extracted from taper curves derived from two-date TLS point clouds. The results of this study showed the capability of point cloud-based methods in detecting changes in the stem form and volume allocation. In addition, the results showed a significant difference between different forest structures in how relative stem volume and logwood volume increased during the monitoring period. Along with contributing to providing more accurate information for monitoring purposes in general, the findings of this study showed the ability and many possibilities of point cloud-based method to characterize changes in living organisms in particular, which further promote the feasibility of using point clouds as an observation method also in ecological studies.

THE USING OF TERRESTRIAL LASER (POINT CLOUD) SCANNING TECHNOLOGIES ON URBAN SCALE: EXAMPLE OF THE HISTORICAL URBAN TEXTURE OF LAPSEKİ

2021 ◽  
Vol 0 (23) ◽  
pp. 0-0
Author(s):  
Hatice Çiğdem ZAĞRA ◽  
Sibel ÖZDEN
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Terrestrial Laser Scanning ◽  
Traditional Methods ◽  
Life Project ◽  
Three Dimensional Models ◽  
Architectural Restoration ◽  
Cloud Technologies ◽  
Orthophoto Images

Aim: This study aims to comparatively evaluate the use potential of orthophoto images obtained by terrestrial laser scanning technologies on an urban scale through the "Old Lapseki Finds Life Project" prepared using terrestrial laser scanning technologies and the "Enez Historical City Square Project" prepared using traditional methods. Method: In the study, street improvement projects of 29.210 m2 Lapseki and 29.214 m2 Enez city designed on an urban scale were evaluated and compared with descriptive statistics based on different parameters. Results: In the study, it has been determined that terrestrial laser (point cloud) technologies are 99,9% accurate when compared to traditional methods, save time by 83,08% and reduce workforce by 80%. In addition, it has been determined that terrestrial laser scanning technologies accelerate project processes compared to traditional methods. Conclusion: In this study, the use of laser scanning technologies, which are basically reverse engineering applications, in architectural restoration projects, determination of the current situation and damage, architectural documentation of structures and preparation of three-dimensional models, in terms of efficiency in survey studies are evaluated. It has been observed that orthophoto images obtained by terrestrial laser scanning technologies in architectural relief-restoration-restitution projects have potentials' worth using in different stages of the project.

scholarly journals Network method for deformation analysis of three-dimensional point cloud with terrestrial laser scanning sensor

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881413 ◽  
Author(s):  
Xiangyang Xu ◽  
Hao Yang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Terrestrial Laser Scanning ◽  
Deformation Analysis ◽  
Transverse Displacement ◽  
Displacement Analysis ◽  
Accuracy Measurement ◽  
Network Method ◽  
Displacement Information

The complexity of structural materials is increasing the importance of the technology for high accuracy measurement. How to obtain the displacement information of structural feature points accurately and efficiently is the key issue of deformation analysis. In this article, displacement analysis of a composite arched structure is investigated based on the terrestrial laser scanning technique. A new method based on the measured point cloud is proposed to analyze the displacement of surficial points, resulting in not only the displacement size but also the displacement direction. The innovation lies in extracting the displacement information with a network and remapped point cloud, which is called the network method. The displacement map obtained demonstrates that the transverse displacement in the experiment plays an important role in the safety of the structure, which could not be observed and obtained by the surface approximation method. Therefore, the panorama- and pointwise displacement analysis technologies contribute to ensure the safety of increasingly complex constructions.

scholarly journals Point-Cloud Models of Historical Barns – Spatial Discrepancies of Laser Scanning versus Robotic Total Station

10.29007/2493 ◽  
2020 ◽  
Author(s):  
Gustavo Maldonado ◽  
Marcel Maghiar ◽  
Brent Tharp ◽  
Dhruv Patel
Keyword(s):  
Service Learning ◽  
Point Cloud ◽  
Laser Scanning ◽  
3D Point Cloud ◽  
Spatial Accuracy ◽  
Total Station ◽  
Cloud Models ◽  
Bulloch County ◽  
Southern University ◽  
Robotic Total Station

This study considers the generation of virtual, 3D point-cloud models of seven deteriorating historical, agricultural barns in Bulloch County, Georgia, USA, for preservation purposes. The work was completed as a service-learning project in a course on Terrestrial Light Detection and Ranging (T-LiDAR), offered at Georgia Southern University. The resulting models and fly-through videos were donated to Bulloch County Historical Society and to the Georgia Southern Museum, to make them available to the general public and future generations. Additionally, one of the seven barns was selected to be extensively measured to estimate the relative spatial accuracy of all seven resulting 3D point-cloud models, with respect to measurements completed with a highly accurate instrument. Three accurate benchmarks were established around it for georeferencing purposes. The positions of 44 points were measured in the field via an accurate, one- second, robotic total-station (RTS) instrument. Also, the coordinates of the same points were acquired from within georeferenced and non-georeferenced point-cloud models. These points defined 259 distances. They were compared to determine their discrepancy statistics. It was observed that this process produced virtual models with an approximate maximum spatial discrepancy of one-half inch (0.5 in) with respect to measurements performed by a highly accurate RTS device. There were no substantial differences in the relative accuracies of the georeferenced and non-georeferenced models.

scholarly journals Combined Use of Terrestrial Laser Scanning and UAV Photogrammetry in Mapping Alpine Terrain

2019 ◽  
Vol 11 (18) ◽  
pp. 2154 ◽  
Author(s):  
Ján Šašak ◽  
Michal Gallay ◽  
Ján Kaňuk ◽  
Jaroslav Hofierka ◽  
Jozef Minár
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Mutual Orientation ◽  
Close Range Photogrammetry ◽  
Combined Use ◽  
Wide Range ◽  
Close Range

Airborne and terrestrial laser scanning and close-range photogrammetry are frequently used for very high-resolution mapping of land surface. These techniques require a good strategy of mapping to provide full visibility of all areas otherwise the resulting data will contain areas with no data (data shadows). Especially, deglaciated rugged alpine terrain with abundant large boulders, vertical rock faces and polished roche-moutones surfaces complicated by poor accessibility for terrestrial mapping are still a challenge. In this paper, we present a novel methodological approach based on a combined use of terrestrial laser scanning (TLS) and close-range photogrammetry from an unmanned aerial vehicle (UAV) for generating a high-resolution point cloud and digital elevation model (DEM) of a complex alpine terrain. The approach is demonstrated using a small study area in the upper part of a deglaciated valley in the Tatry Mountains, Slovakia. The more accurate TLS point cloud was supplemented by the UAV point cloud in areas with insufficient TLS data coverage. The accuracy of the iterative closest point adjustment of the UAV and TLS point clouds was in the order of several centimeters but standard deviation of the mutual orientation of TLS scans was in the order of millimeters. The generated high-resolution DEM was compared to SRTM DEM, TanDEM-X and national DMR3 DEM products confirming an excellent applicability in a wide range of geomorphologic applications.

scholarly journals Separating Leaf and Wood Points in Terrestrial Laser Scanning Data Using Multiple Optimal Scales

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1852 ◽  
Author(s):  
Junjie Zhou ◽  
Hongqiang Wei ◽  
Guiyun Zhou ◽  
Lihui Song
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Geometric Features ◽  
Optimization Strategy ◽  
Running Time ◽  
Multi Scale ◽  
Scale Method ◽  
Optimal Scale

The separation of leaf and wood points is an essential preprocessing step for extracting many of the parameters of a tree from terrestrial laser scanning data. The multi-scale method and the optimal scale method are two of the most widely used separation methods. In this study, we extend the optimal scale method to the multi-optimal-scale method, adaptively selecting multiple optimal scales for each point in the tree point cloud to increase the distinctiveness of extracted geometric features. Compared with the optimal scale method, our method achieves higher separation accuracy. Compared with the multi-scale method, our method achieves more stable separation accuracy with a limited number of optimal scales. The running time of our method is greatly reduced when the optimization strategy is applied.

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