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Mapping and Monitoring of Civil Infrastructures Using LiDAR/Laser Scanning

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (10 April 2023) | Viewed by 16416

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Special Issue Editor

Dr. Mostafa Arastounia

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Guest Editor

Assistant Professor of Geospatial Sciences, Kennesaw State University, Marietta, GA 30060, USA
Interests: point cloud; laser scanning; LiDAR; object recognition; segmentation; modeling; object extraction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Laser scanning/LiDAR technology provides a relatively efficient and precise mapping tool that is being employed both in academic studies and industrial projects. Laser scanning is considered as an emerging cutting-edge technology whose applications are still growing rapidly. Over recent years, laser scanners have been used for mapping and monitoring of civil infrastructure elements including railroads, tunnels, urban environments, buildings, road inventories, and industrial sites. This primarily stems from the fact that laser scanning enables a thorough and precise three-dimensional (3D) documentation of the current state of a structure or an object. Such comprehensive mapping data can potentially be utilized for 3D as-built modeling and monitoring purposes.

Prospective authors are invited to contribute to this Special Issue of Remote Sensing by submitting an original manuscript of their latest innovative research results related to the mapping and monitoring of civil infrastructures using LiDAR/laser scanning. In addition, reviews and contributions are welcomed. Original and innovative contributions may include, but are not limited to:

Mapping of civil infrastructure elements;
Monitoring of civil assets;
New methodologies for extraction and/or modeling of objects from laser scanning data;
Innovative applications of laser scanning in academic studies, engineering, industrial, and construction projects;
Developing novel and computationally efficient algorithms for point cloud processing;
Automated object modeling methods;
Interdisciplinary and higher-level studies on various aspects of employing laser scanning technology such as feasibility, strength, challenges, and effectiveness.

Dr. Mostafa Arastounia
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Laser Scanning
LiDAR
Mapping
Monitoring
Object Modeling
Object Extraction
Point Cloud
Segmentation
Civil Infrastructures

Published Papers (6 papers)

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Research

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34 pages, 16226 KiB

Open AccessArticle

Machine Learning and Deep Learning for the Built Heritage Analysis: Laser Scanning and UAV-Based Surveying Applications on a Complex Spatial Grid Structure

by Dario Billi, Valeria Croce, Marco Giorgio Bevilacqua, Gabriella Caroti, Agnese Pasqualetti, Andrea Piemonte and Michele Russo

Remote Sens. 2023, 15(8), 1961; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15081961 - 07 Apr 2023

Cited by 3 | Viewed by 1718

Abstract

The reconstruction of 3D geometries starting from reality-based data is challenging and time-consuming due to the difficulties involved in modeling existing structures and the complex nature of built heritage. This paper presents a methodological approach for the automated segmentation and classification of surveying outputs to improve the interpretation and building information modeling from laser scanning and photogrammetric data. The research focused on the surveying of reticular, space grid structures of the late 19th–20th–21st centuries, as part of our architectural heritage, which might require monitoring maintenance activities, and relied on artificial intelligence (machine learning and deep learning) for: (i) the classification of 3D architectural components at multiple levels of detail and (ii) automated masking in standard photogrammetric processing. Focusing on the case study of the grid structure in steel named La Vela in Bologna, the work raises many critical issues in space grid structures in terms of data accuracy, geometric and spatial complexity, semantic classification, and component recognition. Full article

(This article belongs to the Special Issue Mapping and Monitoring of Civil Infrastructures Using LiDAR/Laser Scanning)

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19 pages, 70605 KiB

Open AccessArticle

Shield Tunnel Convergence Diameter Detection Based on Self-Driven Mobile Laser Scanning

by Lei Xu, Jian Gong, Jiaming Na, Yuanwei Yang, Zhao Tan, Norbert Pfeifer and Shunyi Zheng

Remote Sens. 2022, 14(3), 767; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030767 - 07 Feb 2022

Cited by 8 | Viewed by 2800

Abstract

The convergence diameter of shield tunnels is detected by ellipse fitting or local curve fitting to cross-section points. However, the tunnel section, which is extruded by an external force, has an irregular elliptical shape, and the waist of the tunnel is often blocked by accessories, resulting in data loss. This study proposes a convergence diameter and radial dislocation detection method based on block-level fitting. The proposed method solves the accuracy degradation caused by the model error and point cloud incompletion. First, the noise points in the tunnel section point cloud are removed using the least trimmed squares method. Second, the tunnel transverse seam is then located using the image edge detection algorithm. Third, the endpoint of the convergence diameter is determined by making a specific segment the center and shifting the detector from the center to the pinpoint. Finally, the convergence diameter and radial dislocation are detected by the endpoints of the segments. The experimental results showed that the absolute detection accuracy of this method was better than 3 mm, and the repeated detection accuracy was better than 2 mm. This result is consistent with prior total station measurements, which are more suitable for practical engineering applications. Full article

(This article belongs to the Special Issue Mapping and Monitoring of Civil Infrastructures Using LiDAR/Laser Scanning)

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17 pages, 3878 KiB

Open AccessArticle

A New Approach for Cylindrical Steel Structure Deformation Monitoring by Dense Point Clouds

by Dongfeng Jia, Weiping Zhang, Yuhao Wang and Yanping Liu

Remote Sens. 2021, 13(12), 2263; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122263 - 09 Jun 2021

Cited by 12 | Viewed by 2411

Abstract

As fundamental load-bearing parts, the cylindrical steel structures of transmission towers relate to the stability of the main structures in terms of topological relation and performance. Therefore, the periodic monitoring of a cylindrical steel structure is necessary to maintain the safety and stability of existing structures in energy transmission. Most studies on deformation analysis are still focused on the process of identifying discrepancies in the state of a structure by observing it at different times, yet relative deformation analysis based on the data acquired in single time has not been investigated effectively. In this study, the piecewise cylinder fitting method is presented to fit the point clouds collected at a single time to compute the relative inclination of a cylindrical steel structure. The standard deviation is adopted as a measure to evaluate the degree of structure deformation. Meanwhile, the inclination rate of each section is compared with the conventional method on the basis of the piecewise cylinder fitting parameters. The validity and accuracy of the algorithm are verified by real transmission tower point cloud data. Experimental results show that the piecewise cylinder fitting algorithm proposed in this research can meet the accuracy requirements of cylindrical steel structure deformation analysis and has high application value in the field of structure deformation monitoring. Full article

(This article belongs to the Special Issue Mapping and Monitoring of Civil Infrastructures Using LiDAR/Laser Scanning)

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18 pages, 1540 KiB

Open AccessArticle

A Critical Analysis of Red Ceramic Blocks Roughness Estimation by 2D and 3D Methods

by Daiana Cristina Metz Arnold, Valéria Costa de Oliveira, Claudio de Souza Kazmierczak, Leandro Tonietto, Camila Werner Menegotto, Luiz Gonzaga, Jr., Cristiano André da Costa and Maurício Roberto Veronez

Remote Sens. 2021, 13(4), 789; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040789 - 21 Feb 2021

Cited by 4 | Viewed by 2085

Abstract

The method of measuring the roughness of ceramic substrates is not consensual, with unsuccessful attempts to associate roughness with the adhesion of coatings because the ceramic blocks have different areas of contact, shapes, and dimensions of the roughness as well as the extrusion process influences the mechanical anisotropy of the block. The goal of this work is a quantification and comparison of roughness data obtained by 2D and 3D methods, evaluating the variations of results between the measurement methods and formulating a critical analysis regarding the quality of the information obtained with each method. For this propose, four sets of ceramic blocks with different firing temperature were produced, in order to provide groups of blocks with different surface topographies in which the roughness was estimated. The roughness measurements were made in 4608 regions, resulting in 1536 values using 2D method and 3072 values using 3D method. In the 2D method for ceramic blocks, the measurement orientation strongly influences the result, depending on the measurement position and orientation. The 3D method generates a higher average value and allows to identify roughness variations typical of the ceramic block. The roughness estimation of a ceramic block surface must be done using the 3D method. Full article

(This article belongs to the Special Issue Mapping and Monitoring of Civil Infrastructures Using LiDAR/Laser Scanning)

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Other

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17 pages, 8229 KiB

Open AccessTechnical Note

Multi-Feature Aggregation for Semantic Segmentation of an Urban Scene Point Cloud

by Jiaqing Chen, Yindi Zhao, Congtang Meng and Yang Liu

Remote Sens. 2022, 14(20), 5134; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14205134 - 14 Oct 2022

Cited by 7 | Viewed by 1991

Abstract

With the rapid development of cities, semantic segmentation of urban scenes, as an important and effective imaging method, can accurately obtain the distribution information of typical urban ground features, reflecting the development scale and the level of greenery in the cities. There are some challenging problems in the semantic segmentation of point clouds in urban scenes, including different scales, imbalanced class distribution, and missing data caused by occlusion. Based on the point cloud semantic segmentation network RandLA-Net, we propose the semantic segmentation networks RandLA-Net++ and RandLA-Net3+. The RandLA-Net++ network is a deep fusion of the shallow and deep features of the point clouds, and a series of nested dense skip connections is used between the encoder and decoder. RandLA-Net3+ is based on the multi-scale connection between the encoder and decoder; it also connects internally within the decoder to capture fine-grained details and coarse-grained semantic information at a full scale. We also propose incorporating dilated convolution to increase the receptive field and compare the improvement effect of different loss functions on sample class imbalance. After verification and analysis of our labeled urban scene LiDAR point cloud dataset—called NJSeg-3D—the mIoU of the RandLA-Net++ and RandLA-Net3+ networks is 3.4% and 3.2% higher, respectively, than the benchmark network RandLA-Net. Full article

(This article belongs to the Special Issue Mapping and Monitoring of Civil Infrastructures Using LiDAR/Laser Scanning)

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13 pages, 3375 KiB

Open AccessCase Report

Systematic Approach for Tunnel Deformation Monitoring with Terrestrial Laser Scanning

by Dongfeng Jia, Weiping Zhang and Yanping Liu

Remote Sens. 2021, 13(17), 3519; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173519 - 04 Sep 2021

Cited by 17 | Viewed by 3318

Abstract

The use of terrestrial laser scanning (TLS) point clouds for tunnel deformation measurement has elicited much interest. However, general methods of point-cloud processing in tunnels are still under investigation, given the high accuracy and efficiency requirements in this area. This study discusses a systematic method of analyzing tunnel deformation. Point clouds from different stations need to be registered rapidly and with high accuracy before point-cloud processing. An orientation method of TLS in tunnels that uses a positioning base made in the laboratory is proposed for fast point-cloud registration. The calibration methods of the positioning base are demonstrated herein. In addition, an improved moving least-squares method is proposed as a way to reconstruct the centerline of a tunnel from unorganized point clouds. Then, the normal planes of the centerline are calculated and are used to serve as the reference plane for point-cloud projection. The convergence of the tunnel cross-section is analyzed, based on each point cloud slice, to determine the safety status of the tunnel. Furthermore, the results of the deformation analysis of a particular shield tunnel site are briefly discussed. Full article

(This article belongs to the Special Issue Mapping and Monitoring of Civil Infrastructures Using LiDAR/Laser Scanning)

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