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Applications of Laser Scanning and Photogrammetry in Civil Engineering and Architecture: Beyond 3D Modeling II

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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 (31 January 2025) | Viewed by 14535

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Special Issue Editors

Dr. Domenico Visintini

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Guest Editor

Department of Engineering and Architecture, University of Udine, 33100 Udine, Italy
Interests: photogrammetry; laser scanning; HBIM; mobile mapping systems; uncrewed aerial vehicle; geomatics data integration; digital mapping and GIS
Special Issues, Collections and Topics in MDPI journals

Dr. Filiberto Chiabrando

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Guest Editor

Department of Architecture and Design, Polytechnic University of Turin, I-10125 Torino, Italy
Interests: uncrewed aerial vehicle; photogrammetry; laser scanning; 3D reconstruction; rapid mapping; 3D modeling; 360° cameras; GIS and remote sensing; sensor integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Terrestrial laser scanning (static and mobile) and photogrammetry are geomatics surveying techniques used for both engineering and architectural purposes. Since these applications can vary widely in terms of their dimension and level of detail, the employed laser scanning and photogrammetric hardware and software should also be very different. In this sense, surveying using an UAV (Uncrewed Aerial Vehicle) equipped with laser scanning and/or photogrammetric sensors can be conducted from a single building to a very large structure, e.g., a long bridge, with variable acquisition conditions and problems, and with high accuracy requirements.

In any case, the final output of both terrestrial/aerial techniques is a dense cloud of millions (or potentially billions) of points, often obtained from a suitable and non-trivial integration. At this step of the processing workflow, the modeling procedures can produce “surface” or “object” models. The second modeling approach, known as “scan-to-BIM”, concerns ongoing problems and requires interventions from users.

Nevertheless, geomatics results are continuously improving, and geomatics boundaries will become increasingly recognised outside their field, overlapping with other engineering and architecture disciplines. Our research topics have to consider application requirements, as well as how to fulfil these while avoiding misuse of the highly accurate and detailed geomatics output. Emerging themes of interest could include optimal integration among systems (UAV and terrestrial data, laser scanner and image/video photogrammetry) and with thermal multi-hyperspectral sensors; suitable integration of geomatic and material data for HBIM applications; metrological analysis of geomatic data for restoration projects; and exploitation of finite elements method (FEM) structural analysis using BIM models.

Dr. Domenico Visintini
Dr. Filiberto Chiabrando
Guest Editors

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Keywords

UAV/terrestrial data integration
laser scanning/photogrammetry integration
MMS (mobile mapping systems)
SLAM
hyperspectral, thermal and multispectral sensors
geomatics/material data integration
different approaches in 3D modeling
scan to BIM
HBIM
metrological analysis
structural analysis

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Published Papers (6 papers)

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22 pages, 18003 KiB

Open AccessArticle

Generalized Extraction of Bolts, Mesh, and Rock in Tunnel Point Clouds: A Critical Comparison of Geometric Feature-Based Methods Using Random Forest and Neural Networks

by Luke Weidner and Gabriel Walton

Remote Sens. 2024, 16(23), 4466; https://doi.org/10.3390/rs16234466 - 28 Nov 2024

Cited by 1 | Viewed by 951

Abstract

Automatically identifying mine and tunnel infrastructure elements, such as rock bolts, from point cloud data improves deformation and quality control analyses and could ultimately contribute to improved safety on engineering projects. However, we hypothesize that existing methods are sensitive to small changes in object characteristics across datasets if trained insufficiently, and previous studies have only investigated single datasets. In this study, we present a cross-site training (generalization) investigation for a multi-class tunnel infrastructure classification task on terrestrial laser scanning data. In contrast to previous work, the novelty of this work is that the models are trained and tested across multiple datasets collected in different tunnels. We used two random forest (RF) implementations and one neural network (NN), as proposed in recent studies, on four datasets collected in different mines and tunnels in the US and Canada. We labeled points as belonging to one of four classes—rock, bolt, mesh, and other—and performed cross-site training experiments to evaluate accuracy differences between sites. In general, we found that the NN and RF models had similar performance to each other, and that same-site classification was generally successful, but cross-site performance was much lower and judged as not practically useful. Thus, our results indicate that standard geometric features are often insufficient for generalized classification of tunnel infrastructure, and these types of methods are most successful when applied to specific individual sites using interactive software for classification. Possible future research directions to improve generalized performance are discussed, including domain adaptation and deep learning methods. Full article

(This article belongs to the Special Issue Applications of Laser Scanning and Photogrammetry in Civil Engineering and Architecture: Beyond 3D Modeling II)

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25 pages, 50037 KiB

Open AccessArticle

Surface Reconstruction from SLAM-Based Point Clouds: Results from the Datasets of the 2023 SIFET Benchmark

by Antonio Matellon, Eleonora Maset, Alberto Beinat and Domenico Visintini

Remote Sens. 2024, 16(18), 3439; https://doi.org/10.3390/rs16183439 - 16 Sep 2024

Cited by 1 | Viewed by 1707

Abstract

The rapid technological development that geomatics has been experiencing in recent years is leading to increasing ease, productivity and reliability of three-dimensional surveys, with portable laser scanner systems based on Simultaneous Localization and Mapping (SLAM) technology, gradually replacing traditional techniques in certain applications. Although the performance of such systems in terms of point cloud accuracy and noise level has been deeply investigated in the literature, there are fewer works about the evaluation of their use for surface reconstruction, cartographic production, and as-built Building Information Model (BIM) creation. The objective of this study is to assess the suitability of SLAM devices for surface modeling in an urban/architectural environment. To this end, analyses are carried out on the datasets acquired by three commercial portable laser scanners in the context of a benchmark organized in 2023 by the Italian Society of Photogrammetry and Topography (SIFET). In addition to the conventional point cloud assessment, we propose a comparison between the reconstructed mesh and a ground-truth model, employing a model-to-model methodology. The outcomes are promising, with the average distance between models ranging from 0.2 to 1.4 cm. However, the surfaces modeled from the terrestrial laser scanning point cloud show a level of detail that is still unmatched by SLAM systems. Full article

(This article belongs to the Special Issue Applications of Laser Scanning and Photogrammetry in Civil Engineering and Architecture: Beyond 3D Modeling II)

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27 pages, 56472 KiB

Open AccessArticle

Robust Fusion of Multi-Source Images for Accurate 3D Reconstruction of Complex Urban Scenes

by Yubin Liang, Yang Yang, Yifan Mu and Tiejun Cui

Remote Sens. 2023, 15(22), 5302; https://doi.org/10.3390/rs15225302 - 9 Nov 2023

Cited by 6 | Viewed by 2618

Abstract

Integrated reconstruction is crucial for 3D modeling urban scenes using multi-source images. However, large viewpoint and illumination variations pose challenges to existing solutions. A novel approach for accurate 3D reconstruction of complex urban scenes based on robust fusion of multi-source images is proposed. Firstly, georeferenced sparse models are reconstructed from the terrestrial and aerial images using GNSS-aided incremental SfM, respectively. Then, cross-platform match pairs are selected based on point-on-image observability. The terrestrial and aerial images are robustly matched based on the selected match pairs to generate cross-platform tie points. Thirdly, the tie points are triangulated to derive cross-platform 3D correspondences. The 3D correspondences are refined using a novel outlier detection method. Finally, the terrestrial and aerial sparse models are merged based on the refined correspondences, and the integrated model is globally optimized to obtain an accurate reconstruction of the scene. The proposed methodology is evaluated on five benchmark datasets, and extensive experiments are performed. The proposed pipeline is compared with a state-of-the-art methodology and three widely used software packages. Experimental results demonstrate that the proposed methodology outperforms the other pipelines in terms of robustness and accuracy. Full article

(This article belongs to the Special Issue Applications of Laser Scanning and Photogrammetry in Civil Engineering and Architecture: Beyond 3D Modeling II)

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24 pages, 27592 KiB

Open AccessArticle

The Frinco Castle: From an Integrated Survey to 3D Modelling and a Stratigraphic Analysis for Helping Knowledge and Reconstruction

by Filippo Diara and Marco Roggero

Remote Sens. 2023, 15(19), 4874; https://doi.org/10.3390/rs15194874 - 8 Oct 2023

Cited by 2 | Viewed by 2642

Abstract

The Frinco Castle (AT-Italy) was the focus of a critical requalification and restoration project and historical knowledge. The initial medieval nucleus was modified and enriched by other architectural parts giving the current shape over the centuries. These additions gave the castle its actual internal and external complexity and an extreme structural fragility: in 2014, a significant portion collapsed. The main objective of this work was to obtain 3D metric documentation and a historical interpretation of the castle for reconstruction and fruition purposes. The local administration has planned knowledge processes from 2021: an integrated 3D geodetic survey of the entire castle and stratigraphic investigations of masonries. Both surveys were essential for understanding the architectural composition as well as the historical evolution of the court. NURBS modelling and a stratigraphic analysis of masonries allowed for the implementation of 3D immersion related to the historical interpretation. Furthermore, this modelling choice was essential for virtually reconstructing the collapsed area and helping the restoration phase. Full article

(This article belongs to the Special Issue Applications of Laser Scanning and Photogrammetry in Civil Engineering and Architecture: Beyond 3D Modeling II)

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21 pages, 8064 KiB

Open AccessArticle

Geometry and Topology Reconstruction of BIM Wall Objects from Photogrammetric Meshes and Laser Point Clouds

by Fan Yang, Yiting Pan, Fangshuo Zhang, Fangyuan Feng, Zhenjia Liu, Jiyi Zhang, Yu Liu and Lin Li

Remote Sens. 2023, 15(11), 2856; https://doi.org/10.3390/rs15112856 - 31 May 2023

Cited by 12 | Viewed by 3015

Abstract

As the foundation for digitalization, building information modeling (BIM) technology has been widely used in the field of architecture, engineering, construction, and facility management (AEC/FM). Unmanned aerial vehicle (UAV) oblique photogrammetry and laser scanning have become increasingly popular data acquisition techniques for surveying buildings and providing original data for BIM modeling. However, the geometric and topological reconstruction of solid walls, which are among the most important architectural structures in BIM, is still a challenging undertaking. Due to noise and missing data in 3D point clouds, current research mostly focuses on segmenting wall planar surfaces from unstructured 3D point clouds and fitting the plane parameters without considering the thickness or 3D shape of the wall. Point clouds acquired only from the indoor space are insufficient for modeling exterior walls. It is also important to maintain the topological relationships between wall objects to meet the needs of complex BIM modeling. Therefore, in this study, a geometry and topology modeling method is proposed for solid walls in BIM based on photogrammetric meshes and laser point clouds. The method uses a kinetic space-partitioning algorithm to generate the building footprint and indoor floor plan. It classifies interior and exterior wall segments and infers parallel line segments to extract wall centerlines. The topological relationships are reconstructed and maintained to build wall objects with consistency. Experimental results on two datasets, including both photogrammetric meshes and indoor laser point clouds, exhibit more than 90% completeness and correctness, as well as centimeter-level accuracy of the wall surfaces. Full article

(This article belongs to the Special Issue Applications of Laser Scanning and Photogrammetry in Civil Engineering and Architecture: Beyond 3D Modeling II)

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15 pages, 6157 KiB

Open AccessTechnical Note

Characterization of Operational Vibrations of Steel-Girder Highway Bridges via LiDAR

by Adriana Trias-Blanco, Jie Gong and Franklin L. Moon

Remote Sens. 2023, 15(4), 1003; https://doi.org/10.3390/rs15041003 - 11 Feb 2023

Cited by 2 | Viewed by 2176

Abstract

This research is motivated by the need for rapidly deployable technologies such as wireless, non-contact or remote sensing for evaluating bridges under operating conditions to minimize the data collection time, avoid the disruption of traffic and increase the inspector’s safety. The objective established for this research is to explore the use of remote sensing (e.g., Light Detection and Ranging (LiDAR)) for characterizing the structural vibration of bridges to support and improve bridge assessment practices. To satisfy this objective, a field study was performed on a 12-span steel stringer bridge in the Philadelphia region. This structure was subjected to extensive LiDAR scanning and conventional vibration data collection through the use of accelerometers for validation purposes. The analysis of the data collected in the field revealed LiDAR’s capability for detecting the structure’s vibration. The field data displayed an error for LiDAR vs. accelerometers of between 1.9 and 10 percent. Additionally, numerical modeling was performed on MATLAB to allow for a better understanding of the interaction between the scanner and the structure. The numerical model presents a vibrating plate to represent a simply supported single-span bridge and a terrestrial LiDAR sensor located under the plate which scans while it is vibrating constantly without attenuation. Finally, a set of recommendations were established for the use of LiDAR scanning to evaluate the structure’s frequency of vibration. Full article

(This article belongs to the Special Issue Applications of Laser Scanning and Photogrammetry in Civil Engineering and Architecture: Beyond 3D Modeling II)

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