Special Issue "Inland Transport Networks Monitoring from Remote Sensing and Photogrammetry"

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

Deadline for manuscript submissions: closed (30 September 2019).

Special Issue Editors

Dr. Belén Riveiro
E-Mail Website
Guest Editor
School Industrial Engineering, University of Vigo, Torrecedeira 86, CP 36208, Vigo, Spain
Tel. +34986130151
Interests: structural health monitoring; structural modeling from remote sensing technologies; point cloud data processing; mobile laser scanning for transport infrastructure inventory and inspection; urban 3D modeling
Special Issues and Collections in MDPI journals
Dr. Mario Soilán
E-Mail Website
Guest Editor
School Industrial Engineering, University of Vigo, Torrecedeira 86, CP 36208 Vigo, Spain
Interests: point cloud data processing; MLS data interpretation for transport infrastructure inventory; machine learning applied to geomatic data; computer vision
Dr. Sander Oude Elberink
E-Mail Website
Guest Editor
Faculty of Geo-Information Science and Earth Observation, University of Twente, Hengelosestraat 99, 7514 AE Enschede, The Netherlands
Interests: point cloud processing; object detection and classification of MLS and ALS point clouds; 3D modelling of buildings; detection and modelling of infrastructural objects; fusing point clouds with large-scale topographic map data
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In the last few years there has been intense research activity regarding the exploitation of remote sensing technologies towards their adoption in civil engineering applications. In the transport sector, it is well known that transport network reliability is limited by infrastructure conditions, and consequently, knowledge about technologies and their effectiveness and costs for medium–long term monitoring are needed. Remote sensing (both terrestrial and satellite) has revealed to be a suitable approach to effectively collect data at a large scale, and with an accuracy level that inland transport networks demand. Data sources from remote sensing include: (i) satellite sensors, which provide coverage of large infrastructure with a high temporal resolution; (ii) terrestrial remote sensing technologies, which provide extremely high spatial resolution; and (iii) contact or embedded sensing that can enrich the information of the infrastructure. Data fusion together with image processing and machine learning approaches have allowed the automated modelling and interpretation of data to be integrated into the infrastructure management systems. The integration of infrastructure BIM with geospatial data, or more recently, the infrastructure information modelling (based in the well-known BIM logic) is also an emerging topic in full life-cycle infrastructure management.

This Special Issue aims at collecting new technologies, data collections and processing methodologies, and successful applications of remote sensing to inland transport monitoring. We welcome submissions which cover, but are not limited to:

  • Remote sensing technologies with potential for the monitoring of large infrastructures, including different platforms (e.g. ,Terrestrial, satellite, aerial, etc.).
  • Evaluation and integration of new 3D and 2D imaging sensors for the purpose of 3D mapping for environmental and infrastructure monitoring.
  • Automated data analysis of 3D data (segmentation, feature extraction, classification, etc.) for the massive inspection of large infrastructure networks. Specially, large-scale Machine Learning applications for transport infrastructure monitoring are envisaged.
  • InSAR applications for structural health monitoring of critical infrastructures, as well as successful applications in large areas such as other infrastructure (ports, airports, cities, etc.).
  • Use of 3D photogrammetric techniques for inspection and life cycle monitoring of infrastructures like bridges, buildings, dikes, and to improve on the integration with structural component analysis.
  • Infrastructure Information Modeling (e.g., BIM for construction projects in infrastructure, applications of existing standards: InfraGML, IFC ROAD, RoadBIM, LandInfra, etc.).
  • Crowdsourcing concepts for the real time inspection of inland transport networks including: exploitation of onboard sensors in connected vehicles, exploitation of social media infrastructure for the collaborative inventory/inspection of infrastructure assets, etc.
  • Generation and update of high-resolution 3D models and databases of road and railways, including mesh based, polyhedral, parametric and multiscale representations and (semantic) attributes. 
  • Performance evaluation of new artificial vision systems for the monitoring of dynamic processes associated to the mechanical or structural behaviour. Technologies and methods for improved deformation analysis in multiscale contexts.
Dr. Belén Riveiro
Dr. Mario Soilán
Dr. Sander Oude Elberink

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 papers will be 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 2000 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

  • Mobile laser scanning
  • Satellite monitoring
  • Infrastructure modelling and monitoring
  • Infrastructure BIM
  • Point cloud processing
  • InSAR
  • Machine learning
  • Structural health monitoring
  • Subsidience
  • Deformation analysis
  • Construction site monitoring

Published Papers (7 papers)

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Research

Open AccessArticle
Automated Inspection of Railway Tunnels’ Power Line Using LiDAR Point Clouds
Remote Sens. 2019, 11(21), 2567; https://doi.org/10.3390/rs11212567 - 01 Nov 2019
Abstract
Transport networks need periodic inspections to increase their safety and improve their management. In the last few years, LiDAR (light detection and ranging) technology has become a tool for helping to create a precise database of almost any type of infrastructure. Mobile laser [...] Read more.
Transport networks need periodic inspections to increase their safety and improve their management. In the last few years, LiDAR (light detection and ranging) technology has become a tool for helping to create a precise database of almost any type of infrastructure. Mobile laser scanning (MLS) systems use a laser beam to collect dense three dimensional (3D) point clouds, which include geometric and radiometric data of the environment in which they are placed. In the context of this paper, a methodology for automatically inspecting the clearance gauge and the deflection of the aerial contact line in railway tunnels is presented. The main objective is to compare results and verify their compliance with the Spanish norm. The 3D data are provided by a LYNX Mobile Mapper System (MMS). First, the area is surveyed and then the obtained (3D) point cloud is classified into contact wire, suspension wire, and remaining points. Finally, the inspection of the railway’s power line is performed. The validation of the proposed methodology has been carried out in three different tunnel point clouds, obtaining both qualitative and quantitative results for points’ classification, together with the results of the measures performed. Full article
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Open AccessFeature PaperArticle
Monitoring Deformation along Railway Systems Combining Multi-Temporal InSAR and LiDAR Data
Remote Sens. 2019, 11(19), 2298; https://doi.org/10.3390/rs11192298 - 02 Oct 2019
Abstract
Multi-temporal interferometric synthetic aperture radar (MT-InSAR) can be applied to monitor the structural health of infrastructure such as railways, bridges, and highways. However, for the successful interpretation of the observed deformation within a structure, or between structures, it is imperative to associate a [...] Read more.
Multi-temporal interferometric synthetic aperture radar (MT-InSAR) can be applied to monitor the structural health of infrastructure such as railways, bridges, and highways. However, for the successful interpretation of the observed deformation within a structure, or between structures, it is imperative to associate a radar scatterer unambiguously with an actual physical object. Unfortunately, the limited positioning accuracy of the radar scatterers hampers this attribution, which limits the applicability of MT-InSAR. In this study, we propose an approach for health monitoring of railway system combining MT-InSAR and LiDAR (laser scanning) data. An amplitude-augmented interferometric processing approach is applied to extract continuously coherent scatterers (CCS) and temporary coherent scatterers (TCS), and estimate the parameters of interest. Based on the 3D confidence ellipsoid and a decorrelation transformation, all radar scatterers are linked to points in the point cloud and their coordinates are corrected as well. Additionally, several quality metrics defined using both the covariance matrix and the radar geometry are introduced to evaluate the results. Experimental results show that most radar scatterers match well with laser points and that LiDAR data are valuable as auxiliary data to classify the radar scatterers. Full article
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Open AccessArticle
Topology-Aware Road Network Extraction via Multi-Supervised Generative Adversarial Networks
Remote Sens. 2019, 11(9), 1017; https://doi.org/10.3390/rs11091017 - 29 Apr 2019
Cited by 1
Abstract
Road network extraction from remote sensing images has played an important role in various areas. However, due to complex imaging conditions and terrain factors, such as occlusion and shades, it is very challenging to extract road networks with complete topology structures. In this [...] Read more.
Road network extraction from remote sensing images has played an important role in various areas. However, due to complex imaging conditions and terrain factors, such as occlusion and shades, it is very challenging to extract road networks with complete topology structures. In this paper, we propose a learning-based road network extraction framework via a Multi-supervised Generative Adversarial Network (MsGAN), which is jointly trained by the spectral and topology features of the road network. Such a design makes the network capable of learning how to “guess” the aberrant road cases, which is caused by occlusion and shadow, based on the relationship between the road region and centerline; thus, it is able to provide a road network with integrated topology. Additionally, we also present a sample quality measurement to efficiently generate a large number of training samples with a little human interaction. Through the experiments on images from various satellites and the comprehensive comparisons to state-of-the-art approaches on the public datasets, it is demonstrated that the proposed method is able to provide high-quality results, especially for the completeness of the road network. Full article
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Open AccessArticle
An Efficient Framework for Mobile Lidar Trajectory Reconstruction and Mo-norvana Segmentation
Remote Sens. 2019, 11(7), 836; https://doi.org/10.3390/rs11070836 - 08 Apr 2019
Cited by 3
Abstract
Mobile laser scanning (MLS, or mobile lidar) is a 3-D data acquisition technique that has been widely used in a variety of applications in recent years due to its high accuracy and efficiency. However, given the large data volume and complexity of the [...] Read more.
Mobile laser scanning (MLS, or mobile lidar) is a 3-D data acquisition technique that has been widely used in a variety of applications in recent years due to its high accuracy and efficiency. However, given the large data volume and complexity of the point clouds, processing MLS data can be still challenging with respect to effectiveness, efficiency, and versatility. This paper proposes an efficient MLS data processing framework for general purposes consisting of three main steps: trajectory reconstruction, scan pattern grid generation, and Mo-norvana (Mobile Normal Variation Analysis) segmentation. We present a novel approach to reconstructing the scanner trajectory, which can then be used to structure the point cloud data into a scan pattern grid. By exploiting the scan pattern grid, point cloud segmentation can be performed using Mo-norvana, which is developed based on our previous work for processing Terrestrial Laser Scanning (TLS) data, normal variation analysis (Norvana). In this work, with an unorganized MLS point cloud as input, the proposed framework can complete various tasks that may be desired in many applications including trajectory reconstruction, data structuring, data visualization, edge detection, feature extraction, normal estimation, and segmentation. The performance of the proposed procedures are experimentally evaluated both qualitatively and quantitatively using multiple MLS datasets via the results of trajectory reconstruction, visualization, and segmentation. The efficiency of the proposed method is demonstrated to be able to handle a large dataset stably with a fast computation speed (about 1 million pts/sec. with 8 threads) by taking advantage of parallel programming. Full article
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Open AccessArticle
A Fast Algorithm for Rail Extraction Using Mobile Laser Scanning Data
Remote Sens. 2018, 10(12), 1998; https://doi.org/10.3390/rs10121998 - 10 Dec 2018
Cited by 4
Abstract
Railroads companies conduct regular inspections of their tracks to maintain and update the geographic data for railway management. Traditional railroad inspection methods, such as onsite inspections and semi-automated analysis of imagery and video data, are time consuming and ineffective. This study presents an [...] Read more.
Railroads companies conduct regular inspections of their tracks to maintain and update the geographic data for railway management. Traditional railroad inspection methods, such as onsite inspections and semi-automated analysis of imagery and video data, are time consuming and ineffective. This study presents an automated effective method to detect tracks on the basis of their physical shape, geometrical properties, and reflection intensity feature. This study aims to investigate the feasibility of fast extraction of railroad using onboard Velodyne puck data collected by mobile laser scanning (MLS) system. Results show that the proposed method can be executed rapidly on an i5 computer with at least 10 Hz. The MLS system used in this study comprises a Velodyne puck/onboard GNSS receiver/inertial measurement unit. The range accuracy of Velodyne puck equipment is 2 cm, which fulfills the need of precise mapping. Notably, positioning STD is lower than 4 cm in most areas. Experiments are also undertaken to evaluate the timing of the proposed method. Experimental results indicate that the proposed method can extract 3D tracks in real-time and correctly recognize pairs of tracks. Accuracy, precision, and sensitivity of total test area are 99.68%, 97.55%, and 66.55%, respectively. Results suggest that in a multi-track area, close collaboration between MLS platforms mounted on several trains is required. Full article
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Open AccessArticle
Extracting Individual Bricks from a Laser Scan Point Cloud of an Unorganized Pile of Bricks
Remote Sens. 2018, 10(11), 1709; https://doi.org/10.3390/rs10111709 - 29 Oct 2018
Abstract
Bricks are the vital component of most masonry structures. Their maintenance is critical to the protection of masonry buildings. Terrestrial Light Detection and Ranging (TLidar) systems provide massive point cloud data in an accurate and fast way. TLidar enables us to sample and [...] Read more.
Bricks are the vital component of most masonry structures. Their maintenance is critical to the protection of masonry buildings. Terrestrial Light Detection and Ranging (TLidar) systems provide massive point cloud data in an accurate and fast way. TLidar enables us to sample and store the state of a brick surface in a practical way. This article aims to extract individual bricks from an unorganized pile of bricks sampled by a dense point cloud. The method automatically segments and models the individual bricks. The methodology is divided into five main steps: Filter needless points, brick boundary points removal, coarse segmentation using 3D component analysis, planar segmentation and grouping, and brick reconstruction. A novel voting scheme is used to segment the planar patches in an effective way. Brick reconstruction is based on the geometry of single brick and its corresponding nominal size (length, width and height). The number of bricks reconstructed is around 75%. An accuracy assessment is performed by comparing 3D coordinates of the reconstructed vertices to the manually picked vertices. The standard deviations of differences along x, y and z axes are 4.55 mm, 4.53 mm and 4.60 mm, respectively. The comparison results indicate that the accuracy of reconstruction based on the introduced methodology is high and reliable. The work presented in this paper provides a theoretical basis and reference for large scene applications in brick-like structures. Meanwhile, the high-accuracy brick reconstruction lays the foundation for further brick displacement estimation. Full article
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Open AccessArticle
3D Vibration Estimation from Ground-Based Radar
Remote Sens. 2018, 10(11), 1670; https://doi.org/10.3390/rs10111670 - 23 Oct 2018
Abstract
The paper proposes a method to estimate 2D/3D vibrations and displacements of mostly linear structures, like pipes, chimneys, towers, bridges from afar, based on synchronized Radars. The method takes advantage of Radar sensitivity to displacements to sense tiny deformations (up to tens of [...] Read more.
The paper proposes a method to estimate 2D/3D vibrations and displacements of mostly linear structures, like pipes, chimneys, towers, bridges from afar, based on synchronized Radars. The method takes advantage of Radar sensitivity to displacements to sense tiny deformations (up to tens of micron) with a time scale from milliseconds to hours. The key elements are: (a) The use of calibrators to remove at once both the tropospheric turbulence and the effect of radial motion, and (b) the compensation of interferences from fixed targets. The latter is performed by estimating and removing the contribution of interfering targets, based either on a proper data processing or by exploiting an ad-hoc motorized calibrator. Performance in terms of accuracy of the deformation field is evaluated theoretically and checked by tests carried out in laboratories and by full-scale acquisition campaigns. Full article
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