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UAVs for Civil Engineering Applications
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UAVs for Civil Engineering Applications

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 13777

Special Issue Editor

Dr. Tamás Lovas

E-Mail Website
Guest Editor
Department of Photogrammetry and Geoinformatics, Budapest University of Technology and Economics, H-1111 Budapest, Hungary
Interests: laser scanning; GIS; remote sensing; mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

 Civil engineering projects (e.g., building construction, road construction, public works) require reliable, accurate spatial data. In many cases, the construction sites are difficult to reach or field measurements would disturb the onsite works. Although the innovation level of civil engineering projects is mostly low, there is a huge potential to improve the level of automation, using building information modeling (that requires accurate geometry as input) and digital twin models (near real-time monitoring of construction sites from the beginning). The as-built models effectively support the operational phase of the building’s lifecycle and facility management. Unmanned aerial vehicles (UAVs) are cost-effective tools for carrying various sensors (cameras, laser scanners) and are able to provide point clouds in a short period of time. In particular, civil engineering design, construction or operational phases, and UAVs potentially replace traditional surveying methods. UAV laser scanning opens new ways for providing terrain models in areas covered by dense vegetation; e.g., in cases of road or power line constructions, huge areas are to be surveyed. In this context, this Special Issue invites research papers discussing the latest developments in applying UAV technologies in any kinds of civil engineering applications. Papers are welcome in the fields of UAV system development, configuration and specification; UAV survey planning; accuracy analysis; and modeling solutions to support specific civil engineering areas (point clouds, surface models, vector products, orthoimages etc.). Guest Editor: Dr. Tamás Lovas

Dr. Tamás Lovas
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

  • Uav
  • UAV Imagery
  • UAV LiDAR
  • Point cloud processing
  • Monitoring
  • Accuracy assesment
  • Civil engineering
  • building construction
  • road construction
  • BIM

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Published Papers (4 papers)

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Research

28 pages, 21848 KiB  
Article
Multi-Instrumental Approach to Slope Failure Monitoring in a Landslide Susceptible Newly Built-Up Area: Topo-Geodetic Survey, UAV 3D Modelling and Ground-Penetrating Radar
by Paul Sestras, Ștefan Bilașco, Sanda Roșca, Ioel Veres, Nicoleta Ilies, Artan Hysa, Velibor Spalević and Sorin M. Cîmpeanu
Remote Sens. 2022, 14(22), 5822; https://doi.org/10.3390/rs14225822 - 17 Nov 2022
Cited by 22 | Viewed by 3761
Abstract
Slope failures and landslides cause economic damage and deaths worldwide. These losses can be minimized by integrating different methodologies, instruments, and data monitoring to predict future landslides. In the constantly growing metropolitan area of Cluj-Napoca, Romania, changes in land cover, land use, and [...] Read more.
Slope failures and landslides cause economic damage and deaths worldwide. These losses can be minimized by integrating different methodologies, instruments, and data monitoring to predict future landslides. In the constantly growing metropolitan area of Cluj-Napoca, Romania, changes in land cover, land use, and build-up areas are an issue. The unprecedented urban sprawl pushed the city limits from the Somes River to hilly terrain prone to landslides and erosion. This study focuses on a landslide-prone area where a previous slope failure caused significant economic losses. It combines topo-geodetic measurements, UAV monitoring of surface displacement, GIS spatial analysis, ground-penetrating radar investigations, and geotechnical assessment. Two years of data show that the slope is undergoing surface erosion, with soil displacements of a few centimeters. Geodetic monitoring of the retaining wall’s control points indicates a small rotation. Coupled with georadar profile imaging showing changes in soil and rock layers with an uplift trend, it was deduced that the area suffers from a global instability. The findings provide valuable information about the dynamics of landslides and erosion for forecasting future movements and developing preventative strategies based on a new methodology that combines affordable and prevalent instrumentation and techniques. Full article
(This article belongs to the Special Issue UAVs for Civil Engineering Applications)
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28 pages, 16093 KiB  
Article
Multidirectional Shift Rasterization (MDSR) Algorithm for Effective Identification of Ground in Dense Point Clouds
by Martin Štroner, Rudolf Urban and Lenka Línková
Remote Sens. 2022, 14(19), 4916; https://doi.org/10.3390/rs14194916 - 1 Oct 2022
Cited by 13 | Viewed by 2310
Abstract
With the ever-increasing popularity of unmanned aerial vehicles and other platforms providing dense point clouds, filters for the identification of ground points in such dense clouds are needed. Many filters have been proposed and are widely used, usually based on the determination of [...] Read more.
With the ever-increasing popularity of unmanned aerial vehicles and other platforms providing dense point clouds, filters for the identification of ground points in such dense clouds are needed. Many filters have been proposed and are widely used, usually based on the determination of an original surface approximation and subsequent identification of points within a predefined distance from such surface. We presented a new filter, the multidirectional shift rasterization (MDSR) algorithm, which is based on a different principle, i.e., on the identification of just the lowest points in individual grid cells, shifting the grid along both the planar axis and subsequent tilting of the entire grid. The principle was presented in detail and both visually and numerically compared with other commonly used ground filters (PMF, SMRF, CSF, and ATIN) on three sites with different ruggedness and vegetation density. Visually, the MDSR filter showed the smoothest and thinnest ground profiles, with the ATIN the only filter comparably performing. The same was confirmed when comparing the ground filtered by other filters with the MDSR-based surface. The goodness of fit with the original cloud is demonstrated by the root mean square deviations (RMSDs) of the points from the original cloud found below the MDSR-generated surface (ranging, depending on the site, between 0.6 and 2.5 cm). In conclusion, this paper introduced a newly developed MDSR filter that outstandingly performed at all sites, identifying the ground points with great accuracy while filtering out the maximum of vegetation and above-ground points and outperforming the aforementioned widely used filters. The filter dilutes the cloud somewhat; in such dense point clouds, however, this can be perceived as a benefit rather than as a disadvantage. Full article
(This article belongs to the Special Issue UAVs for Civil Engineering Applications)
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20 pages, 3527 KiB  
Article
An Approach to Semantically Segmenting Building Components and Outdoor Scenes Based on Multichannel Aerial Imagery Datasets
by Yu Hou, Meida Chen, Rebekka Volk and Lucio Soibelman
Remote Sens. 2021, 13(21), 4357; https://doi.org/10.3390/rs13214357 - 29 Oct 2021
Cited by 6 | Viewed by 2692
Abstract
As-is building modeling plays an important role in energy audits and retrofits. However, in order to understand the source(s) of energy loss, researchers must know the semantic information of the buildings and outdoor scenes. Thermal information can potentially be used to distinguish objects [...] Read more.
As-is building modeling plays an important role in energy audits and retrofits. However, in order to understand the source(s) of energy loss, researchers must know the semantic information of the buildings and outdoor scenes. Thermal information can potentially be used to distinguish objects that have similar surface colors but are composed of different materials. To utilize both the red–green–blue (RGB) color model and thermal information for the semantic segmentation of buildings and outdoor scenes, we deployed and adapted various pioneering deep convolutional neural network (DCNN) tools that combine RGB information with thermal information to improve the semantic and instance segmentation processes. When both types of information are available, the resulting DCNN models allow us to achieve better segmentation performance. By deploying three case studies, we experimented with our proposed DCNN framework, deploying datasets of building components and outdoor scenes, and testing the models to determine whether the segmentation performance had improved or not. In our observation, the fusion of RGB and thermal information can help the segmentation task in specific cases, but it might also make the neural networks hard to train or deteriorate their prediction performance in some cases. Additionally, different algorithms perform differently in semantic and instance segmentation. Full article
(This article belongs to the Special Issue UAVs for Civil Engineering Applications)
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18 pages, 2824 KiB  
Article
Monitoring the Work Cycles of Earthmoving Excavators in Earthmoving Projects Using UAV Remote Sensing
by Yiguang Wu, Meizhen Wang, Xuejun Liu, Ziran Wang, Tianwu Ma, Zhimin Lu, Dan Liu, Yujia Xie, Xiuquan Li and Xing Wang
Remote Sens. 2021, 13(19), 3853; https://doi.org/10.3390/rs13193853 - 26 Sep 2021
Cited by 9 | Viewed by 3296
Abstract
Monitoring the work cycles of earthmoving excavators is an important aspect of construction productivity assessment. Currently, the most advanced method for the recognition of work cycles is the “Stretching-Bending” Sequential Pattern (SBSP), which is based on fixed-carrier video monitoring (FC-SBSP). However, the application [...] Read more.
Monitoring the work cycles of earthmoving excavators is an important aspect of construction productivity assessment. Currently, the most advanced method for the recognition of work cycles is the “Stretching-Bending” Sequential Pattern (SBSP), which is based on fixed-carrier video monitoring (FC-SBSP). However, the application of this method presupposes the availability of preconstructed installation carriers to act as a surveillance camera as well as installed and commissioned surveillance systems that work in tandem with them. Obviously, this method is difficult to apply to projects with no conditions for a monitoring camera installation or which have a short construction time. This highlights the potential application of Unmanned Aerial Vehicle (UAV) remote sensing, which is flexible and mobile. Unfortunately, few studies have been conducted on the application of UAV remote sensing for the work cycle monitoring of earthmoving excavators. This research is necessary because the use of UAV remote sensing for monitoring the work cycles of earthmoving excavators can improve construction productivity and save time and costs, especially in post-disaster reconstruction projects involving harsh construction environments, and emergency projects with short construction periods. In addition, the challenges posed by UAV shaking may have to be taken into account when using the SBSP for UAV remote sensing. To this end, this study used application experiments in which stabilization processing of UAV video data was performed for UAV shaking. The application experimental results show that the work cycle performance of UAV remote-sensing-based SBSP (UAV-SBSP) for UAV video data was 2.45% and 5.36% lower in terms of precision and recall, respectively, without stabilization processing than after stabilization processing. Comparative experiments were also designed to investigate the applicability of the SBSP oriented toward UAV remote sensing. Comparative experimental results show that the same level of performance was obtained for the recognition of work cycles with the UAV-SBSP as compared with the FC-SBSP, demonstrating the good applicability of this method. Therefore, the results of this study show that UAV remote sensing enables effective monitoring of earthmoving excavator work cycles in construction sites where monitoring cameras are not available for installation, and it can be used as an alternative technology to fixed-carrier video monitoring for onsite proximity monitoring. Full article
(This article belongs to the Special Issue UAVs for Civil Engineering Applications)
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