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Remote Sensing in Civil and Environmental Engineering

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

Deadline for manuscript submissions: 15 July 2024 | Viewed by 11997

Special Issue Editors

1. School of Computing and Engineering, University of West London, Room BY.03.19, St. Mary’s Rd., Ealing, London W5 5RF, UK
2. The Faringdon Centre for Non-Destructive Testing and Remote Sensing, University of West London, Room BY.GF.015, St. Mary’s Rd., Ealing, London W5 5RF, UK
Interests: ground-penetrating radar; signal processing; remote sensing; deflection-based methods; numerical simulations; forestry engineering; airfield and highway pavement engineering; construction materials; civil engineering
Special Issues, Collections and Topics in MDPI journals
Department of Civil, Computer Science and Aeronautical Technologies Engineering, Roma Tre University, Rome, Italy
Interests: ground-penetrating radar; signal processing; remote sensing; deflection-based assessment methods; non-destructive testing; modeling and simulation; road safety and highway engineering; driving simulation; civil engineering
Special Issues, Collections and Topics in MDPI journals
Italian Space Agency (ASI), Via del Politecnico snc, 00133 Rome, Italy
Interests: earth observation; radar and optical remote sensing; InSAR; time series analysis; earth sciences; environmental geology; natural hazards; urban environments; geoheritage; geoconservation; cultural heritage
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Civil engineering structures are assets that are vital to human life in terms of the economy, mobility, the environment, and the development of communities. It is of no doubt that assets should be maintained and cared for as part of a robust, planned monitoring and maintenance programme within the context of the life cycle of structures and their interaction with the environment. It is imperative that any adopted assessment and monitoring methods should be cost-effective, efficient, and fit for purpose. Depending on the infrastructure type and needs, different approaches should be used to generate useful information for long-term sustainability. In this framework, remote sensing technologies have been proven to be instrumental in providing vital information about structures’ performance and behaviour as well as environmental changes. Their applications are numerous and currently include the structural health monitoring of civil infrastructure systems (buildings, bridges, roads, railways, and airfields), the excavation and tunnelling-induced settlements of buildings, preventive archaeology, natural hazards, and hydrology and water resources. On the other hand, the number of remote sensing applications for environmental monitoring and conservation ecology are growing at present and mainly relate to the monitoring of vegetation, biomass, forest carbon, erosive processes, and pollution.

This Special Issue aims to provide a comprehensive overview of state-of-the-art applications and numerical, theoretical, and industrial developments of remote sensing technologies and methods in the civil and environmental engineering areas of science.

The followings are areas of interest and priority for this Special Issue:      

  • Platforms (ground-borne, space-borne, and air-borne);
  • Orbit types (geo-synchronous, sun-synchronous);
  • Sensor types and systems (active/passive sensors, framing/scanning systems, multi-spectral imaging/thermal remote sensing/microwave radar sensing systems);
  • Advanced image processing (data fusion and integration of multi-sensor data, image segmentation and classification algorithms, feature selection algorithms, change detection and multi-temporal analysis, geographic object-based image analysis);
  • Enhanced data analysis and interpretation methods (machine learning and deep learning techniques for time-series analysis and forecasting models of deformations);
  • The integration of remote sensing data into GIS;
  • Technology and data-driven integration between remote sensing and non-destructive testing methods (e.g., ground penetrating radar, laser scanning, deflection-based methods, infrared thermography);
  • The development of fully deployed and prototype remote sensing hardware and software;
  • New satellite missions and downstream applications;
  • The contribution of remote sensing for the development of new standards, policies and best practices.

Prof. Dr. Fabio Tosti
Prof. Dr. Andrea Benedetto
Dr. Deodato Tapete
Guest Editors

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

  • civil and environmental engineering
  • ground-borne, space-borne, and air-borne remote sensing platforms
  • infrastructure monitoring and preventive archaeology
  • hydrology and water resources
  • natural hazards
  • environmental monitoring and conservation ecology
  • enhanced image processing, data analysis, and interpretation methods
  • remote sensing technology and data-driven integration with NDTs
  • remote sensing and GIS
  • new satellite missions and downstream applications

Published Papers (5 papers)

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Research

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26 pages, 21105 KiB  
Article
High-Temporal-Resolution Rock Slope Monitoring Using Terrestrial Structure-from-Motion Photogrammetry in an Application with Spatial Resolution Limitations
by Bradford Butcher, Gabriel Walton, Ryan Kromer, Edgard Gonzales, Javier Ticona and Armando Minaya
Remote Sens. 2024, 16(1), 66; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16010066 - 23 Dec 2023
Viewed by 552
Abstract
Research on high-temporal-resolution rock slope monitoring has tended to focus on scenarios where spatial resolution is also high. Accordingly, there is a lack of understanding of the implications for rock slope monitoring results in cases with high temporal resolution but low spatial resolution, [...] Read more.
Research on high-temporal-resolution rock slope monitoring has tended to focus on scenarios where spatial resolution is also high. Accordingly, there is a lack of understanding of the implications for rock slope monitoring results in cases with high temporal resolution but low spatial resolution, which is the focus of this study. This study uses automatically captured photos taken at a daily frequency by five fixed-base cameras in conjunction with multi-epoch Structure-from-Motion (SfM) photogrammetric processing techniques to evaluate changes in a rock slope in Majes, Arequipa, Peru. The results of the monitoring campaign demonstrate that there are potential issues with the common notion that higher frequency change detection is always superior. For lower spatial resolutions or when only large changes are of concern, using a high-frequency monitoring method may cause small volume changes that eventually aggrade into larger areas of change to be missed, whereas most of the total volume change would be captured with lower-frequency monitoring intervals. In this study, daily change detection and volume calculation resulted in a cumulative rockfall volume of 4300 m3 over about 14 months, while change detection and volume calculation between dates at the start and end of the 14-month period resulted in a total rockfall volume of 12,300 m3. High-frequency monitoring is still the most accurate approach for evaluating slope evolution from a rockfall frequency and size distribution perspective, and it allows for the detection of short accelerations and pre-failure deformations, but longer-term comparison intervals may be required in cases where spatial resolution is low relative to temporal resolution to more accurately reflect the total volume change of a given rock slope over a long period of time. Full article
(This article belongs to the Special Issue Remote Sensing in Civil and Environmental Engineering)
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40 pages, 33041 KiB  
Article
A Handheld LiDAR-Based Semantic Automatic Segmentation Method for Complex Railroad Line Model Reconstruction
by Junjie Chen, Qian Su, Yunbin Niu, Zongyu Zhang and Jinghao Liu
Remote Sens. 2023, 15(18), 4504; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15184504 - 13 Sep 2023
Cited by 1 | Viewed by 1023
Abstract
To ensure efficient railroad operation and maintenance management, the accurate reconstruction of railroad BIM models is a crucial step. This paper proposes a workflow for automated segmentation and reconstruction of railroad structures using point cloud data, without relying on intensity or trajectory information. [...] Read more.
To ensure efficient railroad operation and maintenance management, the accurate reconstruction of railroad BIM models is a crucial step. This paper proposes a workflow for automated segmentation and reconstruction of railroad structures using point cloud data, without relying on intensity or trajectory information. The workflow consists of four main components: point cloud adaptive denoising, scene segmentation, structure segmentation combined with deep learning, and model reconstruction. The proposed workflow was validated using two datasets with significant differences in railroad line point cloud data. The results demonstrated significant improvements in both efficiency and accuracy compared to existing methods. The techniques enable direct automated processing from raw data to segmentation results, providing data support for parameterized modeling and greatly reducing manual processing time. The proposed algorithms achieved an intersection over union (IoU) of over 0.9 for various structures in a 450-m-long railroad line. Furthermore, for single-track railroads, the automated segmentation time was within 1 min per kilometer, with an average mean intersection over union (MIoU) and accuracy of 0.9518 and 1.0000, respectively. Full article
(This article belongs to the Special Issue Remote Sensing in Civil and Environmental Engineering)
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29 pages, 13569 KiB  
Article
Robust LiDAR-Based Vehicle Detection for On-Road Autonomous Driving
by Xianjian Jin, Hang Yang, Xiongkui He, Guohua Liu, Zeyuan Yan and Qikang Wang
Remote Sens. 2023, 15(12), 3160; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15123160 - 17 Jun 2023
Cited by 3 | Viewed by 1716
Abstract
The stable detection and tracking of high-speed vehicles on the road by using LiDAR can input accurate information for the decision-making module and improve the driving safety of smart cars. This paper proposed a novel LiDAR-based robust vehicle detection method including three parts: [...] Read more.
The stable detection and tracking of high-speed vehicles on the road by using LiDAR can input accurate information for the decision-making module and improve the driving safety of smart cars. This paper proposed a novel LiDAR-based robust vehicle detection method including three parts: point cloud clustering, bounding box fitting and point cloud recognition. Firstly, aiming at the problem of clustering quality degradation caused by the uneven distribution of LiDAR point clouds and the difference in clustering radius between point cloud clusters in traditional DBSCAN (TDBSCAN) obstacle clustering algorithms, an improved DBSCAN algorithm based on distance-adaptive clustering radius (ADBSCAN) is designed, and a point cloud KD-Tree data structure is constructed to speed up the traversal of the algorithm; meanwhile, the OPTICS algorithm is introduced to enhance the performance of the proposed algorithm. Then, by adopting different fitting strategies for vehicle contour points in various states, the adaptability of the bounding box fitting algorithm is improved; Moreover, in view of the shortcomings of the poor robustness of the L-shape algorithm, the principal component analysis method (PCA) is introduced to obtain stable bounding box fitting results. Finally, considering the time-consuming and low-accuracy training of traditional machine learning algorithms, advanced PointNet in deep learning technique is built to send the point cloud within the bounding box of a high-confidence vehicle into PointNet to complete vehicle recognition. Experiments based on our autonomous driving perception platform and the KITTI dataset prove that the proposed method can stably complete vehicle target recognition and achieve a good balance between time-consuming and accuracy. Full article
(This article belongs to the Special Issue Remote Sensing in Civil and Environmental Engineering)
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18 pages, 6703 KiB  
Article
Differential Settlement of Track Foundations Identification Based on GRU Neural Network
by Jiqing Jiang, Liang Ding, Yuhui Zhou and He Zhang
Remote Sens. 2023, 15(9), 2378; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15092378 - 30 Apr 2023
Viewed by 1407
Abstract
The timely identification of differential settlement of track foundations is of great significance for the safety of train operation and the maintenance of track structures. However, traditional monitoring techniques cannot meet the requirements of efficient, real-time, and automatic monitoring of track foundation settlement. [...] Read more.
The timely identification of differential settlement of track foundations is of great significance for the safety of train operation and the maintenance of track structures. However, traditional monitoring techniques cannot meet the requirements of efficient, real-time, and automatic monitoring of track foundation settlement. In order to solve these problems, a real-time identification method based on a gated recurrent unit (GRU) neural network is proposed for the differential settlement of track foundations monitoring. According to parameter sensitivity analysis, the vertical acceleration of the vehicle is selected as the known data fed into the GRU network for differential settlement identification. Then the GRU network is employed to establish the nonlinear relationship between the vertical acceleration of the vehicle and the differential settlement of the track foundation. The results indicate that the longitudinal continuous differential settlement distribution curve of track foundations could be accurately identified with GRU neural network through the real-time vibration response of the vehicle–track. The current method may provide a new means for the real-time and efficient identification of the differential settlement of track foundations. Full article
(This article belongs to the Special Issue Remote Sensing in Civil and Environmental Engineering)
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Review

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29 pages, 7286 KiB  
Review
Satellite Remote Sensing and Non-Destructive Testing Methods for Transport Infrastructure Monitoring: Advances, Challenges and Perspectives
by Valerio Gagliardi, Fabio Tosti, Luca Bianchini Ciampoli, Maria Libera Battagliere, Luigi D’Amato, Amir M. Alani and Andrea Benedetto
Remote Sens. 2023, 15(2), 418; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020418 - 10 Jan 2023
Cited by 20 | Viewed by 5166
Abstract
High-temporal-frequency monitoring of transport infrastructure is crucial to facilitate maintenance and prevent major service disruption or structural failures. Ground-based non-destructive testing (NDT) methods have been successfully applied for decades, reaching very high standards for data quality and accuracy. However, routine campaigns and long [...] Read more.
High-temporal-frequency monitoring of transport infrastructure is crucial to facilitate maintenance and prevent major service disruption or structural failures. Ground-based non-destructive testing (NDT) methods have been successfully applied for decades, reaching very high standards for data quality and accuracy. However, routine campaigns and long inspection times are required for data collection and their implementation into reliable infrastructure management systems (IMSs). On the other hand, satellite remote sensing techniques, such as the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) method, have proven effective in monitoring ground displacements of transport infrastructure (roads, railways and airfields) with a much higher temporal frequency of investigation and the capability to cover wider areas. Nevertheless, the integration of information from (i) satellite remote sensing and (ii) ground-based NDT methods is a subject that is still to be fully explored in civil engineering. This paper aims to review significant stand-alone and combined applications in these two areas of endeavour for transport infrastructure monitoring. The recent advances, main challenges and future perspectives arising from their mutual integration are also discussed. Full article
(This article belongs to the Special Issue Remote Sensing in Civil and Environmental Engineering)
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