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Remote Sensing
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Transport Infrastructure Monitoring Based on Remote Sensing
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Transport Infrastructure Monitoring Based on Remote Sensing

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

Deadline for manuscript submissions: closed (1 July 2023) | Viewed by 4823

Special Issue Editors

Prof. Dr. Massimo Ramondini

E-Mail Website
Guest Editor
Department of Civil Engineering, Architectural and Environmental of Federico II University of Napoli, Via Claudio 21, 80125 Naples, Italy
Interests: landslide; remote sensing; monitoring; DInSAR; vulnerability; risk assessment
Prof. Dr. Diego Di Martire

E-Mail Website
Guest Editor
Department of Earth Sciences, Environment and Resources, University of Naples Federico II, 80126 Napoli, Italy
Interests: landslides, hazard and risk assessment; interferometry SAR; GIS
Special Issues, Collections and Topics in MDPI journals
Dr. Donato Infante

E-Mail Website
Guest Editor
Sintema Engineering srl, Spin Off University of Naples Federico II, via Toledo 156, 80138 Naples, Italy
Interests: structural health monitoring; remote sensing; DInSAR; vulnerability assessment

Special Issue Information

Dear Colleagues,

The transport infrastructure network (roads, highways, and railways) represents a connection system of noteworthy importance for the social and economic life of any nation and plays a significant role in the success of its economy.

The occurrence of geological events such as landslides, for instance, is one of the main causes of damage along linear infrastructures. Damage to transport infrastructures, such as roads, bridges, and railways, can inhibit their optimal functioning and contribute to traffic accidents.

Frequent and accurate monitoring of slope instability phenomena and their interaction with existing infrastructures plays a fundamental role in risk prevention and mitigation activities.

The monitoring and control demands technicians and the procedures of traditional maintenance teams, who detect, through visual inspection, anomalies and failures that could represent a critical condition for users. This approach, in addition to representing a significant portion of the owner's annual budget, may not be effective due to the long time lag between onsite data collection and the transfer of information to the operations center.

Interferometric synthetic aperture radar (InSAR) is an alternative technique to obtain measurements of surface displacement, providing better spatial resolution and comparable accuracy at an extremely lower cost per area than conventional surveying methods. InSAR is becoming increasingly popular in monitoring urban and infrastructure deformations, even if the technique requires advanced tools and a high level of competence to be successfully applied.

Satellite monitoring systems, based on the use of radar images, can offer a valuable source of independent information products to support infrastructure health assessments. Compared with traditional detection techniques, they allow obtaining a high density of measurement points over large areas for a much lower cost per area. Furthermore, thanks to the evolution of processing techniques (PSInSAR, SqueeSAR), displacement time series can be analyzed with very high accuracy.

This Special Issue aims to illustrate and discuss different uses of the interferometric synthetic aperture radar (InSAR) technique in transportation infrastructure planning and monitoring. Topics may cover anything from the project analysis to monitoring during its life and in planning surveillance and maintenance programs.

Hence, multisource data integration and comparison (traditional data such as topography, GPS, inclinometer measurements, and InSAR time series), monitoring and surveillance infrastructures systems, innovative approaches or studies focused on SAR data applications to transport infrastructures monitoring, among other issues, are welcome. Articles may address, but are not limited, to the following topics:

  • structural health monitoring
  • vulnerability assessment
  • hazard assessment
  • risk awareness
  • geotechnical monitoring
  • transportation
  • interferometric SAR
  • natural hazards
  • remote sensing

Prof. Dr. Massimo Ramondini
Prof. Dr. Diego Di Martire
Dr. Donato Infante
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

  • structural health monitoring
  • vulnerability
  • InSAR
  • natural hazards
  • monitoring
  • land management

Published Papers (3 papers)

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Research

18 pages, 7347 KiB  
Article
Performance Assessment of Structural Monitoring of a Dedicated High-Speed Railway Bridge Using a Moving-Base RTK-GNSS Method
by Ruijie Xi, Weiping Jiang, Wei Xuan, Dongsheng Xu, Jian Yang, Lihua He and Jun Ma
Remote Sens. 2023, 15(12), 3132; https://doi.org/10.3390/rs15123132 - 15 Jun 2023
Cited by 1 | Viewed by 1104
Abstract
At present, high-precision GNSS positioning technology is an important means to monitor the health of bridges and other structures. However, the GNSS signal of reference stations and monitoring stations used for bridge monitoring can easily be blocked by bridge towers, vehicles, or other [...] Read more.
At present, high-precision GNSS positioning technology is an important means to monitor the health of bridges and other structures. However, the GNSS signal of reference stations and monitoring stations used for bridge monitoring can easily be blocked by bridge towers, vehicles, or other objects, resulting in low positioning accuracy and insufficient availability of GNSS, which affects the effectiveness of bridge structural health monitoring. Therefore, according to the characteristics of bridge structure, this paper proposes to take the bridge tower monitoring station as a moving-base station to build the baselines with other monitoring stations and use the moving-base RTK-GNSS method to realize the relative positioning, so as to improve the availability of GNSS in the application of bridge structure health monitoring. In this paper, the moving-base RTK-GNSS model is derived and verified via GNSS monitoring data of the Ganzhou dedicated high-speed railway bridge. The results show that the ambiguity in the fixing rate can be improved using the moving-base RTK-GNSS method with the tower monitoring station as the reference station. The deformation and vibration characteristics of each monitoring point can be reflected, and the displacement and vibration amplitude estimation accuracy can achieve results better than 4 mm. Therefore, the moving-base RTK-GNSS method can be used as an alternative scheme when the observation environment of the base station is poor or the banded engineering monitoring is applied, so as to improve the monitoring capability of GNSS. Full article
(This article belongs to the Special Issue Transport Infrastructure Monitoring Based on Remote Sensing)
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23 pages, 10487 KiB  
Article
Exploring Airborne LiDAR and Aerial Photographs Using Machine Learning for Land Cover Classification
by Ming-Da Tsai, Kuan-Wen Tseng, Chia-Cheng Lai, Chun-Ta Wei and Ken-Fa Cheng
Remote Sens. 2023, 15(9), 2280; https://doi.org/10.3390/rs15092280 - 26 Apr 2023
Cited by 1 | Viewed by 1512
Abstract
Airborne LiDAR is a popular measurement technology in recent years. Its feature is that it can quickly acquire high precision and high density 3D point coordinates on the surface. The reflective waveform of the radar contains the geometric structure and roughness of the [...] Read more.
Airborne LiDAR is a popular measurement technology in recent years. Its feature is that it can quickly acquire high precision and high density 3D point coordinates on the surface. The reflective waveform of the radar contains the geometric structure and roughness of the surface reflector. Combined with the information from aerial photographs, it can quickly help users to interpret various surface object types and serve as a basis for land cover classification. The experiment is divided into three phases. In the phase 1, LiDAR data and decision tree classification method (DT) were used to classify the land cover and customize the geometric parameter elevation. In the phase 2, we combined aerial photographs, LiDAR data and DT method to improve the accuracy of land cover classification. In the phase 3, the support vector machine classification method (SVM) was used to compare the classification accuracy of different classification methods. The results show that customizing the geometric parameter elevation can improve the overall classification accuracy. The results of the study showed that the DT method and the SVM method had better results for the grass, building and artificial ground, and the SVM method had better results for the planted shrub and bare ground. Full article
(This article belongs to the Special Issue Transport Infrastructure Monitoring Based on Remote Sensing)
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15 pages, 5375 KiB  
Article
Enhancing the Thermal Images of the Upper Scarp of the Poggio Baldi Landslide (Italy) by Physical Modeling and Image Analysis
by Andrea Massi, Michele Ortolani, Domenico Vitulano, Vittoria Bruni and Paolo Mazzanti
Remote Sens. 2023, 15(4), 907; https://doi.org/10.3390/rs15040907 - 06 Feb 2023
Cited by 2 | Viewed by 1367
Abstract
We present new methods for physical interpretation and mathematical treatment of the imaging contrast observed in thermal images of the rocky upper scarp of the Poggio Baldi landslide (Italy), which is part of a natural laboratory. Exemplar thermal images have been acquired with [...] Read more.
We present new methods for physical interpretation and mathematical treatment of the imaging contrast observed in thermal images of the rocky upper scarp of the Poggio Baldi landslide (Italy), which is part of a natural laboratory. Exemplar thermal images have been acquired with a high-performance camera at a distance of around 500 m, in a geometry where reflection is expected to dominate over thermal emission. The digital pixel intensities have therefore been considered as wavelength-integrated infrared spectral reflectance, irrespective of the temperature scale loaded into the camera software. Sub-portions of the scarp producing a lower signal have been identified by a multiscale image segmentation algorithm and overlaid on the visible image to provide an interpretation for the different thermal imaging contrast mechanisms that may be exploited for landslide monitoring in the future. Full article
(This article belongs to the Special Issue Transport Infrastructure Monitoring Based on Remote Sensing)
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