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Applications of Remote Sensing to Inland Transportation Infrastructure Monitoring and Intelligent Transport System Planning

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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 (20 March 2023) | Viewed by 23332

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Special Issue Editors

Dr. Belen Riveiro

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Guest Editor

CINTEX, GeoTech Lab, Campus Universitario de Vigo, Universidade de Vigo, As Lagoas, Marcosende, 36310 Vigo, Spain
Interests: laser scanning; structural monitoring; resilience of infrastructure
Special Issues, Collections and Topics in MDPI journals

Dr. Mario Soilán

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Guest Editor

Department of Cartographic and Land Engineering, University of Salamanca, Higher Polytechnic School of Avila, Hornos Caleros 50, 05003 Avila, Spain
Interests: laser scanning; Infrastructure monitoring; BIM; IIM; IFC
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Transport infrastructures represent assets that are especially vulnerable to the increasing extreme events (both natural and human-made). Deficiencies in maintenance operations are often behind the malfunction or collapse of infrastructure assets. The structural deficiencies of assets normally show signs of deterioration that would allow anticipating the occurrence of failures and thus would help to prevent accidents and optimize maintenance expenditures. In this sense, the operators and owners of the infrastructures of developed countries are investing more and more in the deployment of innovative technologies of a different nature, in parallel with the digitization of the transport sector. This leads to vast amounts of data, but they are not always efficiently and comprehensively managed. Digital technologies are a prerequisite for improving monitoring practices. It is widely recognized that digitization will play a critical role in the near future in capturing and operating vast amounts of data. Therefore, it is imperative to adopt methods capable of digesting all these data, implementing big data analysis strategies, including artificial intelligence, while ensuring interoperability between the different information models generated.

Surveying technologies, including both terrestrial and satellite remote sensing, have been extensively adopted for the condition monitoring of critical infrastructures in recent decades. In recent years, there has been an intense research activity not only evaluating single platforms of data sources, but also remote sensing, which is playing a key role in multidisciplinary projects where multiscale and multidimensional approaches are being proposed for large-scale infrastructure monitoring. Artificial Intelligence (AI) is also an emerging topic in remote sensing, accelerating the adoption of remotely sensed data by experts from outside of the geomatics domain. This is motivated by the extensive capabilities of many branches of AI, such as machine learning or deep learning, to automatically and efficiently handle, process, and model large datasets. The successful extraction of information (geometry, semantics, and topology) from geospatial data has noticeably impacted the deployment of infrastructure BIM.

This Special Issue aims at compiling the latest developments in automated processing of various remote sensing datasets using the aforementioned AI techniques in applications focused on the monitoring of inland transportation networks and planning of intelligent transport systems.

Dr. Belen Riveiro
Dr. Mario Soilán
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

InSAR
Mobile laser scanning
Point cloud processing
Machine learning
Deep learning
Infrastructure BIM
Digital twins

Published Papers (10 papers)

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Research

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0 pages, 9501 KiB

Open AccessArticle

An Infrared Small Target Detection Method Based on a Weighted Human Visual Comparison Mechanism for Safety Monitoring

by Yuanyuan Chen, Huiqian Wang, Yu Pang, Jinhui Han, En Mou and Enling Cao

Remote Sens. 2023, 15(11), 2922; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15112922 - 03 Jun 2023

Cited by 3 | Viewed by 1462 | Retraction

Abstract

Infrared small target detection is a crucial technology in both military and civilian applications, including surveillance, security, defense, and combat. However, accurate infrared detection of small targets in real-time is challenging due to their small size and similarity in gray level and texture with the surrounding environment, as well as interference from the infrared imaging systems in unmanned aerial vehicles (UAVs). This article proposes a weighted local contrast method based on the contrast mechanism of the human visual system. Initially, a combined contrast ratio is defined that stems from the pixel-level divergence between the target and its neighboring pixels. Then, an improved regional intensity level is used to establish a weight function with the concept of ratio difference combination, which can effectively suppress complex backgrounds and random noise. In the final step, the contrast and weight functions are combined to create the final weighted local contrast method (WRDLCM). This method does not require any preconditioning and can enhance the target while suppressing background interference. Additionally, it is capable of detecting small targets even when their scale changes. In the experimental section, our algorithm was compared with some popular methods, and the experimental findings indicated that our method showed strong detection capability based on the commonly used performance indicators of the ROC curve, SCRG, and BSF, especially in low signal-to-noise ratio situations. In addition, unlike deep learning, this method is appropriate for small sample sizes and is easy to implement on FPGA hardware. Full article

(This article belongs to the Special Issue Applications of Remote Sensing to Inland Transportation Infrastructure Monitoring and Intelligent Transport System Planning)

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18 pages, 52527 KiB

Open AccessArticle

Sensor-Aided Calibration of Relative Extrinsic Parameters for Outdoor Stereo Vision Systems

by Jing Wang, Banglei Guan, Yongsheng Han, Zhilong Su, Qifeng Yu and Dongsheng Zhang

Remote Sens. 2023, 15(5), 1300; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15051300 - 26 Feb 2023

Cited by 2 | Viewed by 1368

Abstract

Calibration of the stereo vision systems is a crucial step for precise 3D measurements. Restricted by the outdoors’ large field of view (FOV), the conventional method based on precise calibration boards is not suitable since the calibration process is time consuming and the calibration accuracy is not guaranteed. In this paper, we propose a calibration method for estimating the extrinsic parameters of the stereo vision system aided by an inclinometer and a range sensor. Through the parameters given by the sensors, the initial rotation angle of the extrinsic parameters and the translation vector are pre-established by solving a set of linear equations. The metric scale of the translation vector is determined by the baseline length provided by the range sensor or GNSS signals. Finally, the optimal extrinsic parameters of the stereo vision systems are obtained by nonlinear optimization of inverse depth parameterization. The most significant advantage of this method is that it enhances the capability of the stereo vision measurement in the outdoor environment, and can achieve fast and accurate calibration results. Both simulation and outdoor experiments have verified the feasibility and correctness of this method, and the relative error in the outdoor large FOV was less than 0.3%. It shows that this calibration method is a feasible solution for outdoor measurements with a large FOV and long working distance. Full article

(This article belongs to the Special Issue Applications of Remote Sensing to Inland Transportation Infrastructure Monitoring and Intelligent Transport System Planning)

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21 pages, 9572 KiB

Open AccessArticle

Pavement Crack Detection and Clustering via Region-Growing Algorithm from 3D MLS Point Clouds

by Pablo del Río-Barral, Mario Soilán, Silvia María González-Collazo and Pedro Arias

Remote Sens. 2022, 14(22), 5866; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225866 - 19 Nov 2022

Cited by 6 | Viewed by 2720

Abstract

Road condition monitoring plays a critical role in transportation infrastructure maintenance and traffic safety assurance. This research introduces a methodology to detect cracks on pavement point clouds acquired with Mobile Laser Scanning systems, which offer more versatility and comprehensive information about the road environment than other specific surveying systems (i.e., profilometers, 3D cameras). The methodology comprises the following steps: (1) Road segmentation; (2) the detection of candidate crack points in individual scanning lines of the point cloud, based on point elevation; (3) crack point clustering via a region-growing algorithm; and (4) crack geometrical attributes extraction. Both the profile evaluation and the region-growing clustering algorithms have been developed from scratch to detect cracks directly from 3D point clouds instead of using raster data or Geo-Referenced Feature images, offering a quick and effective pre-rating tool for pavement condition assessment. Crack detection is validated with data from damaged roads in Portugal. Full article

(This article belongs to the Special Issue Applications of Remote Sensing to Inland Transportation Infrastructure Monitoring and Intelligent Transport System Planning)

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25 pages, 27148 KiB

Open AccessArticle

Enhancing Railway Detection by Priming Neural Networks with Project Exaptations

by Felix Eickeler and André Borrmann

Remote Sens. 2022, 14(21), 5482; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215482 - 31 Oct 2022

Cited by 1 | Viewed by 1422

Abstract

When integrating railway constructions and refurbishments into an existing infrastructure, it is beneficial to have knowledge of the exact state, geometry, and placement of the connected assets. While new constructions and the maintenance of existing lines can directly use existing digital models and incorporate them into their processes, existing railways often predate digital technologies. This gap in digital models leaves the planning processes of new constructions and refurbishments to primarily rely on non-automated and analogue workflows. With a multitude of asset types, layouts and country-specific standards, the automatic generation of adequate detection models is complicated and needs to be tailored to the current project environment, generating considerable overhead. Addressing this issue, this paper presents the concept of priming. Priming increases the adaptation performance to highly volatile, low-data environments by leveraging previous, existing CAD projects. We introduce a translation scheme that converts the existing 3D models into realistic, project-specific, synthetic surveys and a complemental dialled-in training routine. When applied to a convolutional neural network, we show that the primed training will converge faster and with greater stability, especially when using sparse training data. Our experiments show that priming can reduce the time for network adaptation by over 50%, while also improving resilience to underrepresented object types. Full article

(This article belongs to the Special Issue Applications of Remote Sensing to Inland Transportation Infrastructure Monitoring and Intelligent Transport System Planning)

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23 pages, 5732 KiB

Open AccessArticle

A Deep Learning Based Method for Railway Overhead Wire Reconstruction from Airborne LiDAR Data

by Lele Zhang, Jinhu Wang, Yueqian Shen, Jian Liang, Yuyu Chen, Linsheng Chen and Mei Zhou

Remote Sens. 2022, 14(20), 5272; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14205272 - 21 Oct 2022

Cited by 5 | Viewed by 2295

Abstract

Automatically and accurately reconstructing the overhead wires of railway from airborne laser scanning (ALS) data are an efficient way of railway monitoring to ensure stable and safety transportation services. However, due to the complex structure of the overhead wires, it is challenging to extract these wires using the existing methods. This work proposes a workflow for railway overhead wire reconstruction using deep learning for wire identification collaborating with the RANdom SAmple Consensus (RANSAC) algorithm for wire reconstruction. First, data augmentation and ground points down-sampling are performed to facilitate the issues caused by insufficient and non-uniformity of LiDAR points. Then, a network incorporating with PointNet model is proposed to segment wires, pylons and ground points. The proposed network is composed of a Geometry Feature Extraction (GFE) module and a Neighborhood Information Aggregation (NIA) module. These two modules are introduced to encode and describe the local geometric features. Therefore, the capability of the model to discriminate geometric details is enhanced. Finally, a wire individualization and multi-wire fitting algorithm is proposed to reconstruct the overhead wires. A number of experiments are conducted using ALS point cloud data of railway scenarios. The results show that the accuracy and MIoU for wire identification are 96.89% and 82.56%, respectively, which demonstrates a better performance compared to the existing methods. The overall reconstruction accuracy is 96% over the study area. Furthermore, the presented strategy also demonstrated its applicability to high-voltage powerline scenarios. Full article

(This article belongs to the Special Issue Applications of Remote Sensing to Inland Transportation Infrastructure Monitoring and Intelligent Transport System Planning)

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20 pages, 43746 KiB

Open AccessArticle

Monitoring the Impact of Large Transport Infrastructure on Land Use and Environment Using Deep Learning and Satellite Imagery

by Marko Pavlovic, Slobodan Ilic, Nenad Antonic and Dubravko Culibrk

Remote Sens. 2022, 14(10), 2494; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102494 - 23 May 2022

Cited by 2 | Viewed by 2757

Abstract

Large-scale infrastructure, such as China–Europe Railway Express (CER-Express), which connects countries and regions across Asia and Europe, has a potentially profound effect on land use, as evidenced by changes in land cover along the railway. To ensure sustainable development of such infrastructure and appropriate land administration, effective ways to monitor and assess its impact need to be developed. Remote sensing based on publicly available satellite imagery represents an obvious choice. In the study presented here, we employ a state-of-the-art deep-learning-based approach to automatically detect different types of land cover based on multispectral Sentinel-2 imagery. We then use these data to conduct and present a study of the changes in land use in two geopolitically diverse regions of interest (in Serbia and China and with and without CER-Express infrastructure) for the period of the last three years. Our results show that the standard image-patch-based land cover classification approaches suffer a significant drop in performance in our target scenario in which each pixel needs to be assigned a cove class, but still, validate the applicability of the proposed approach as a remote sensing tool to support the sustainable development of large infrastructure. We discuss the technical limitations of the proposed approach in detail and potential ways in which it can be improved. Full article

(This article belongs to the Special Issue Applications of Remote Sensing to Inland Transportation Infrastructure Monitoring and Intelligent Transport System Planning)

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18 pages, 9577 KiB

Open AccessArticle

Detection of Direct Sun Glare on Drivers from Point Clouds

by Silvia María González-Collazo, Pablo del Río-Barral, Jesús Balado and Elena González

Remote Sens. 2022, 14(6), 1456; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14061456 - 18 Mar 2022

Cited by 4 | Viewed by 2395

Abstract

Sunlight conditions can reduce drivers’ visibility, which is a safety concern on road networks. This research introduces a method to study sun glare incidence in road environments. Sun glare areas during daylight hours are automatically detected from mobile laser scanning (MLS) and aerial laser scanning (ALS) point clouds. The method comprises the following steps. First, the Sun’s position (solar altitude and azimuth) referring to a location is calculated. Second, the incidence of sun glare with the user’s angle of vision is analyzed based on human vision. Third, sun ray intersections with near obstacles (vegetation, building, etc.) are calculated utilizing MLS point clouds. Finally, intersections with distant obstacles (mountains) are calculated utilizing ALS point clouds. MLS and ALS data are processed in order to combine both data types, remove outliers, and optimize computational time for intersection searches (point density reduction and Delaunay triangulation). The method was tested on two real case studies, covering roads with different bearings, slopes, and surroundings. The combination of MLS and ALS data, together with the solar geometry, identify areas of risk for the visibility of drivers. Consequently, the proposed method can be utilized to reduce sun glare, implementing warnings in navigation systems. Full article

(This article belongs to the Special Issue Applications of Remote Sensing to Inland Transportation Infrastructure Monitoring and Intelligent Transport System Planning)

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16 pages, 5162 KiB

Open AccessArticle

Region-Level Traffic Prediction Based on Temporal Multi-Spatial Dependence Graph Convolutional Network from GPS Data

by Haiqiang Yang, Xinming Zhang, Zihan Li and Jianxun Cui

Remote Sens. 2022, 14(2), 303; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020303 - 10 Jan 2022

Cited by 32 | Viewed by 3094

Abstract

Region-level traffic information can characterize dynamic changes of urban traffic at the macro level. Real-time region-level traffic prediction help city traffic managers with traffic demand analysis, traffic congestion control, and other activities, and it has become a research hotspot. As more vehicles are equipped with GPS devices, remote sensing data can be collected and used to conduct data-driven region-level-based traffic prediction. However, due to dynamism and randomness of urban traffic and the complexity of urban road networks, the study of such issues faces many challenges. This paper proposes a new deep learning model named TmS-GCN to predict region-level traffic information, which is composed of Graph Convolutional Network (GCN) and Gated Recurrent Unit (GRU). The GCN part captures spatial dependence among regions, while the GRU part captures the dynamic change of traffic within the region. Model verification and comparison are carried out using real taxi GPS data from Shenzhen. The experimental results show that the proposed model outperforms both the classic time series prediction model and the deep learning model at different scales. Full article

(This article belongs to the Special Issue Applications of Remote Sensing to Inland Transportation Infrastructure Monitoring and Intelligent Transport System Planning)

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24 pages, 5946 KiB

Open AccessArticle

The Heterogeneous Impact of High-Speed Railway on Urban Expansion in China

by Dan He, Zixuan Chen, Jing Zhou, Ting Yang and Linlin Lu

Remote Sens. 2021, 13(23), 4914; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234914 - 03 Dec 2021

Cited by 4 | Viewed by 2843

Abstract

High-speed railway (HSR) promote the efficient flow of the population and materials between cities and have profoundly affected urban economic development in China. However, there is currently limited research about how HSR influences urban expansion, especially related to the variable impacts on different urban agglomerations, different size cities, and the conversion of non-urban land to urban land. In this study, from two levels of regional heterogeneity and type heterogeneity, a multi-stage difference-in-differences (multi-stage DID) model and land use remote sensing data are used to investigate these research areas. The main conclusions are as follows: (1) The first opening of HSR had a more significant role in promoting urban expansion than HSR frequency, but several years after opening, HSR no longer promotes urban expansion. (2) The opening of HSR only played a significant role in promoting urban expansion in Beijing–Tianjin–Hebei. HSR frequency had a significant role in promoting urban expansion in the Yangtze River Delta. (3) The opening of HSR had no significant impact on urban expansion for different size cities, and HSR frequency only had a significant negative impact on urban expansion of small cities. (4) The first opening of HSR led to urban expansion dominated by the occupation of cultivated land. Cities in Xinjiang and Inner Mongolia mainly converted barren land and vegetation cover to urban land after the first opening of HSR. In northeast China, the first opening of HSR made the conversion of vegetation cover and cultivated land to urban land roughly equivalent in size. The results of this study are helpful to understand the impact of the first opening of HSR and the scale of conversion of different types of non-urban land into urban land on urban expansion. In the era of HSR, these findings provide a valuable reference for regional planning and preventing the disorderly expansion of cities. Full article

(This article belongs to the Special Issue Applications of Remote Sensing to Inland Transportation Infrastructure Monitoring and Intelligent Transport System Planning)

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18 pages, 7902 KiB

Open AccessTechnical Note

Polarization Orientation Method Based on Remote Sensing Image in Cloudy Weather

by Jiasai Luo, Sen Zhou, Yiming Li, Yu Pang, Zhengwen Wang, Yi Lu, Huiqian Wang and Tong Bai

Remote Sens. 2023, 15(5), 1225; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15051225 - 22 Feb 2023

Cited by 2 | Viewed by 1334

Abstract

Autonomous navigation technology is a core technology for intelligent operation, allowing the vehicles to perform tasks without relying on external information, which effectively improves the concealability and reliability. In this paper, based on the previous research on the bionic compound eye, a multi-channel camera array with different polarization degrees was used to construct the atmospheric polarization state measurement platform. A polarization trough threshold segmentation algorithm was applied to study the distribution characteristics and characterization methods of polarization states in atmospheric remote sensing images. In the extracted polarization feature map, the tilting suggestion box was obtained based on the multi-direction window extraction network (similarity-based region proposal networks, SRPN) and the rotation of the suggestion box (Rotation Region of interests, RRoIs). Fast Region Convolutional Neural Networks (RCNN) was used to screen the suggestion boxes, and the Non-maximum suppression (NMS) method was used to select the angle, corresponding to the label of the suggestion box with the highest score, as the solar meridian azimuth in the vehicle coordinate system. The azimuth angle of the solar meridian in the atmospheric coordinate system can be calculated by the astronomical formula. Finally, the final heading angle can be obtained according to the conversion relationship between the coordinate systems. By fitting the measured data based on the least Square method, the slope K value is −1.062, RMSE (Root Mean Square Error) is 6.984, and the determination coefficient R-Square is 0.9968. Experimental results prove the effectiveness of the proposed algorithm, and this study can construct an autonomous navigation algorithm with high concealment and precision, providing a new research idea for the research of autonomous navigation technology. Full article

(This article belongs to the Special Issue Applications of Remote Sensing to Inland Transportation Infrastructure Monitoring and Intelligent Transport System Planning)

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