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Road Detection, Monitoring and Maintenance Using Remotely Sensed Data

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 24764

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Prof. Massimo Losa

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Guest Editor

Department of Civil and Industrial Engineering (DICI), The University of Pisa, 56122 Pisa, Italy
Interests: road monitoring; road maintenance; Pavement Management Systems (PMS); tyre road interaction; Non-Destructive Techniques (NDTs); Synthetic Aperture Radar Interferometry (InSAR); Falling Weight Deflectometer (FWD); Ground Penetrating Radar (GPR); laser profilometer; statistical modeling
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Dr. Nicholas Fiorentini

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Guest Editor

Department of Civil and Industrial Engineering (DICI), The University of Pisa, Largo Lucio Lazzarino 1, 56122, Pisa, Italy
Interests: road safety; accident analysis; road monitoring; road maintenance; non-destructive techniques (NDTs); synthetic aperture radar interferometry (InSAR); falling weight deflectometer (FWD); ground penetrating radar (GPR); laser profiler; statistical modelling; machine learning algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Several aspects of the daily lives of most people and communities are connected with roads; they offer a critical contribution to economic development and constitute the social fabric of a developed country. If roads are inadequately managed, maintained and controlled, they constrain mobility and cause increased transportation operating costs, accident rates, and related human and property costs, in addition to aggravating segregation, poverty, and poor health. Unfortunately, in many countries, road authorities are supported by limited funds for road monitoring and inspection, especially for minor and local road networks. Therefore, the hazard prevention, planning, monitoring, inspection, and maintenance of roads network is critical.

Non-destructive and high-performance techniques are essential tools for managing extended and complex road networks. Using these techniques, road authorities can efficiently obtain reliable information concerning the causes of distress (exogenous and endogenous factors) and the consequences (infrastructure damages and deficiencies) for the assets they manage. However, these techniques, if performed individually, do not allow the obtainment of a comprehensive understanding of the functional and structural properties of an infrastructure. Indeed, each NDT is appointed to investigate a single aspect of the infrastructure; integrating the outcomes of multiple NDTs is of fundamental importance for a global vision.

Therefore, the present Special Issue, “Road Detection, Monitoring and Maintenance Using Remotely Sensed Data”, aims to gather studies covering the latest developments in the use and integration of non-destructive techniques for road detection, monitoring and maintenance. The leading goal of this Special Issue is to provide a compendium of innovative operational strategies and relevant case studies for road authorities, identifying the most appropriate remote surveys as a function of the investigated infrastructure typology, the methodologies for integrating road surveys with ancillary data, and providing insights for optimizing decision-making processes regarding road monitoring and maintenance.

Topics of interest include (but are not limited to) the following:

Road monitoring of road networks by remote techniques (satellite-, aerial-, ground-, and subsoil-based non-destructive techniques);
Relevant remote non-destructive techniques applications in transport infrastructures;
Integration of remotely sensed data in pavement maintenance;
Analysis, quantification, and integration of environmental impacts within pavement management systems;
Integration of remote non-destructive technique outcomes and ancillary data sources (topography, geology, hydrology, geomorphology);
Machine/deep learning algorithms for the fusion of remote non-destructive techniques data in road detection, road monitoring, and road maintenance.

Review papers in the outlined above research topics will also be considered.

Prof. Massimo Losa
Dr. Nicholas Fiorentini
Guest Editors

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Keywords

Road monitoring and maintenance;
Remote sensing of infrastructures;
Non-destructive techniques (NDTs);
Synthetic aperture radar interferometry (InSAR);
Falling weight deflectometer (FWD);
Ground-penetrating radar (GPR);
Unmanned aerial vehicles (UAVs) imagery;
Pavement management systems (PMSs);
Artificial intelligence in road monitoring;
Data integration and data fusion in road monitoring.

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Related Special Issue

Road Detection, Monitoring and Maintenance Using Remotely Sensed Data (2nd Edition) in Remote Sensing

Published Papers (10 papers)

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Research

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20 pages, 5967 KiB

Open AccessArticle

Prediction of Unpaved Road Conditions Using High-Resolution Optical Satellite Imagery and Machine Learning

by Robin Workman, Patrick Wong, Alex Wright and Zhao Wang

Remote Sens. 2023, 15(16), 3985; https://doi.org/10.3390/rs15163985 - 11 Aug 2023

Cited by 3 | Viewed by 2268

Abstract

Rural roads play a crucial role in fostering economic and social development in Africa. Local Road Authorities (LRAs) struggle to collect road condition data using conventional means due to logistical and resource issues. Poor road conditions and restricted mobility have severe economic consequences for the transport of goods and services. Lack of maintenance can increase costs three-fold. In this work, a novel framework is proposed in which earth observations using high-resolution optical satellite imagery are applied to measure the condition of unpaved roads, providing a vital input to maintenance planning and prioritisation. A trial was conducted using this method on 83 roads in Tanzania totalling 131.7 km. The experimental results demonstrate that, by analysing variations in pixel intensity of the road surface, the condition can be estimated with an accuracy of 71.9% when compared to ground truth information. Machine Learning techniques are applied to the same network to test the performance of the system in predicting road conditions. A blended classifier approach achieves an accuracy of 88%. The proposed framework enables LRAs to define the information they receive based on their specific priorities, offering a rapid, objective, consistent and potentially cost-effective system that overcomes the current challenges faced by LRAs. Full article

(This article belongs to the Special Issue Road Detection, Monitoring and Maintenance Using Remotely Sensed Data)

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28 pages, 10433 KiB

Open AccessArticle

Combining Images and Trajectories Data to Automatically Generate Road Networks

by Xiangdong Bai, Xuyu Feng, Yuanyuan Yin, Mingchun Yang, Xingyao Wang and Xue Yang

Remote Sens. 2023, 15(13), 3343; https://doi.org/10.3390/rs15133343 - 30 Jun 2023

Cited by 3 | Viewed by 2232

Abstract

Road network data are an important part of many applications, e.g., intelligent transportation and urban planning. At present, most of the approaches to road network generation are dominated by single data sources including images, point cloud data, trajectories, etc., which may cause the fragmentation of information. This study proposes a novel strategy to obtain the vector data of road networks by combining images and trajectory data with a postprocessing method named RNITP. The designed RNITP includes two parts: an initial generation layer of road network detection and a postprocessing layer of vector map acquirement. At the first layer, there are three steps of road network detection including road information interpretation from images based on a new deep learning model (denoted as SPBAM-LinkNet), road detection from trajectories data by rasterizing, and road information fusion by using OR operation. The last layer is used to generate a vector map based on a postprocessing method that is focused on error identification and removal. Experiments were conducted using two kinds of datasets: CHN6-CUG road datasets and HB road datasets. The results show that the accuracy, F1 score, and MIoU of SPBAM-LinkNet on CHN6-CUG and HB were (0.9695, 0.7369, 0.7760) and (0.9387, 0.7257, 0.7514), respectively, which are better than other typical models (e.g., Unet, DeepLabv3+, D-Linknet, NL-Linknet). In addition, the F1 score, IoU, and recall of the vector map obtained from RNITP are 0.8883, 0.7991, and 0.9065, respectively. Full article

(This article belongs to the Special Issue Road Detection, Monitoring and Maintenance Using Remotely Sensed Data)

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33 pages, 24854 KiB

Open AccessArticle

A New Region-Based Minimal Path Selection Algorithm for Crack Detection and Ground Truth Labeling Exploiting Gabor Filters

by Gonzalo de León, Nicholas Fiorentini, Pietro Leandri and Massimo Losa

Remote Sens. 2023, 15(11), 2722; https://doi.org/10.3390/rs15112722 - 24 May 2023

Cited by 7 | Viewed by 1609

Abstract

Cracks are fractures or breaks that occur in materials such as concrete, metals, rocks, and other solids. Various methods are used to detect and monitor cracks; among many of them, image-based methodologies allow fast identification of the distress and easy quantification of the percentage of cracks in the scene. Two main categories can be identified: classical and deep learning approaches. In the last decade, the tendency has moved towards the use of the latter. Even though they have proven their outstanding predicting performance, they suffer some drawbacks: a “black-box” nature leaves the user blind and without the possibility of modifying any parameters, a huge amount of labeled data is generally needed, a process that requires expert judgment is always required, and, finally, they tend to be time-consuming. Accordingly, the present study details the methodology for a new algorithm for crack segmentation based on the theory of minimal path selection combined with a region-based approach obtained through the segmentation of texture features extracted using Gabor filters. A pre-processing step is described, enabling the equalization of brightness and shadows, which results in better detection of local minima. These local minimal are constrained by a minimum distance between adjacent points, enabling a better coverage of the cracks. Afterward, a region-based segmentation technique is introduced to determine two areas that are used to determine threshold values used for rejection. This step is critical to generalize the algorithm to images presenting close-up scenes or wide cracks. Finally, a geometrical thresholding step is presented, allowing the exclusion of rounded areas and small isolated cracks. The results showed a very competitive F1-score (0.839), close to state-of-the-art values achieved with deep learning techniques. The main advantage of this approach is the transparency of the workflow, contrary to what happens with deep learning frameworks. In the proposed approach, no prior information is required; however, the statistical parameters may have to be adjusted to the particular case and requirements of the situation. The proposed algorithm results in a useful tool for researchers and practitioners needing to validate their results against some reference or needing labeled data for their models. Moreover, the current study could establish the grounds to standardize the procedure for crack segmentation with a lower human bias and faster results. The direct application of the methodology to images obtained with any low-cost sensor makes the proposed algorithm an operational support tool for authorities needing crack detection systems in order to monitor and evaluate the current state of the infrastructures, such as roads, tunnels, or bridges. Full article

(This article belongs to the Special Issue Road Detection, Monitoring and Maintenance Using Remotely Sensed Data)

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24 pages, 5745 KiB

Open AccessArticle

State-Level Mapping of the Road Transport Network from Aerial Orthophotography: An End-to-End Road Extraction Solution Based on Deep Learning Models Trained for Recognition, Semantic Segmentation and Post-Processing with Conditional Generative Learning

by Calimanut-Ionut Cira, Miguel-Ángel Manso-Callejo, Ramón Alcarria, Borja Bordel Sánchez and Javier González Matesanz

Remote Sens. 2023, 15(8), 2099; https://doi.org/10.3390/rs15082099 - 16 Apr 2023

Cited by 6 | Viewed by 2109

Abstract

Most existing road extraction approaches apply learning models based on semantic segmentation networks and consider reduced study areas, featuring favorable scenarios. In this work, an end-to-end processing strategy to extract the road surface areas from aerial orthoimages at the scale of the national territory is proposed. The road mapping solution is based on the consecutive execution of deep learning (DL) models trained for ① road recognition, ② semantic segmentation of road surface areas, and ③ post-processing of the initial predictions with conditional generative learning, within the same processing environment. The workflow also involves steps such as checking if the aerial image is found within the country’s borders, performing the three mentioned DL operations, applying a

p = 0.5

decision limit to the class predictions, or considering only the central 75% of the image to reduce prediction errors near the image boundaries. Applying the proposed road mapping solution translates to operations aimed at checking if the latest existing cartographic support (aerial orthophotos divided into tiles of 256 × 256 pixels) contains the continuous geospatial element, to obtain a linear approximation of its geometry using supervised learning, and to improve the initial semantic segmentation results with post-processing based on image-to-image translation. The proposed approach was implemented and tested on the openly available benchmarking SROADEX dataset (containing more than 527,000 tiles covering approximately 8650 km² of the Spanish territory) and delivered a maximum increase in performance metrics of 10.6% on unseen, testing data. The predictions on new areas displayed clearly higher quality when compared to existing state-of-the-art implementations trained for the same task. Full article

(This article belongs to the Special Issue Road Detection, Monitoring and Maintenance Using Remotely Sensed Data)

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19 pages, 9266 KiB

Open AccessArticle

Evaluating Pavement Lane Markings in Metropolitan Road Networks with a Vehicle-Mounted Retroreflectometer and AI-Based Image Processing Techniques

by Sangyum Lee and Byoung Hooi Cho

Remote Sens. 2023, 15(7), 1812; https://doi.org/10.3390/rs15071812 - 28 Mar 2023

Cited by 3 | Viewed by 2151

Abstract

The objectives of this study were to evaluate pavement lane markings in a metropolitan road network and to develop a maintenance strategy for safe daytime and night-time driving. To achieve this, data on the retroreflectivity and physical defect ratio of lane markings were collected remotely using a vehicle-mounted retroreflectometer and high-resolution camera. The retroreflectivity was measured and analyzed by road type (city freeways, arterial roads, and collector roads) and by lane color (yellow, white, and blue) over a total length of 6790.34 km. The results indicate that the retroreflective performance deteriorates the most in the case of white lanes, regardless of the road classification, especially in the case of the first white lane. Additionally, the physical defects of lane markings were investigated over a total length of 502.82 km and categorized by road classification and lane color. Mask R-CNN and the Otsu Threshold method were used to automatically calculate the ratios of the defects. The results indicate that city freeways show a lower defect ratio than arterial and collector roads for all colors. Moreover, there is no significant difference between the white lanes for all types of roads. The distribution trends and relationship between retroreflectivity and the defect ratios were discussed according to the road type and lane color, and a method for selecting maintenance priority was suggested. The results show that the number of lanes requiring the restoration of retroreflectivity increases as the defect ratio increases. Therefore, we suggest prioritizing maintenance work on the lanes with a higher ratio of defects, covering a higher proportion of low-retroreflectivity sections. In addition, the unit length for data averaging can be adjusted to improve the work efficiency. Full article

(This article belongs to the Special Issue Road Detection, Monitoring and Maintenance Using Remotely Sensed Data)

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19 pages, 31101 KiB

Open AccessArticle

Deep Learning for Improved Subsurface Imaging: Enhancing GPR Clutter Removal Performance Using Contextual Feature Fusion and Enhanced Spatial Attention

by Yi Li, Pengfei Dang, Xiaohu Xu and Jianwei Lei

Remote Sens. 2023, 15(7), 1729; https://doi.org/10.3390/rs15071729 - 23 Mar 2023

Cited by 3 | Viewed by 2191

Abstract

In engineering practice, ground penetrating radar (GPR) records are often hindered by clutter resulting from uneven underground media distribution, affecting target signal characteristics and precise positioning. To address this issue, we propose a method combining deep learning preprocessing and reverse time migration (RTM) imaging. Our preprocessing approach introduces a novel deep learning framework for GPR clutter, enhancing the network’s feature-capture capability for target signals through the integration of a contextual feature fusion module (CFFM) and an enhanced spatial attention module (ESAM). The superiority and effectiveness of our algorithm are demonstrated by RTM imaging comparisons using synthetic and laboratory data. The processing of actual road data further confirms the algorithm’s significant potential for practical engineering applications. Full article

(This article belongs to the Special Issue Road Detection, Monitoring and Maintenance Using Remotely Sensed Data)

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18 pages, 7699 KiB

Open AccessArticle

An Integrated Method for Road Crack Segmentation and Surface Feature Quantification under Complex Backgrounds

by Lu Deng, An Zhang, Jingjing Guo and Yingkai Liu

Remote Sens. 2023, 15(6), 1530; https://doi.org/10.3390/rs15061530 - 10 Mar 2023

Cited by 20 | Viewed by 3929

Abstract

In the present study, an integrated framework for automatic detection, segmentation, and measurement of road surface cracks is proposed. First, road images are captured, and crack regions are detected based on the fifth version of the You Only Look Once (YOLOv5) algorithm; then, a modified Residual Unity Networking (Res-UNet) algorithm is proposed for accurate segmentation at the pixel level within the crack regions; finally, a novel crack surface feature quantification algorithm is developed to determine the pixels of crack in width and length, respectively. In addition, a road crack dataset containing complex environmental noise is produced. Different shooting distances, angles, and lighting conditions are considered. Validated through the same dataset and compared with You Only Look at CoefficienTs ++ (YOLACT++) and DeepLabv3+, the proposed method shows higher accuracy for crack segmentation under complex backgrounds. Specifically, the crack damage detection based on the YOLOv5 method achieves a mean average precision of 91%; the modified Res-UNet achieves 87% intersection over union (IoU) when segmenting crack pixels, 6.7% higher than the original Res-UNet; and the developed crack surface feature algorithm has an accuracy of 95% in identifying the crack length and a root mean square error of 2.1 pixels in identifying the crack width, with the accuracy being 3% higher in length measurement than that of the traditional method. Full article

(This article belongs to the Special Issue Road Detection, Monitoring and Maintenance Using Remotely Sensed Data)

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20 pages, 12960 KiB

Open AccessArticle

Automatic Road Inventory Using a Low-Cost Mobile Mapping System and Based on a Semantic Segmentation Deep Learning Model

by Hugo Tardy, Mario Soilán, José Antonio Martín-Jiménez and Diego González-Aguilera

Remote Sens. 2023, 15(5), 1351; https://doi.org/10.3390/rs15051351 - 28 Feb 2023

Cited by 5 | Viewed by 2264

Abstract

Road maintenance is crucial for ensuring safety and government compliance, but manual measurement methods can be time-consuming and hazardous. This work proposes an automated approach for road inventory using a deep learning model and a 3D point cloud acquired by a low-cost mobile mapping system. The road inventory includes the road width, number of lanes, individual lane widths, superelevation, and safety barrier height. The results are compared with a ground truth on a 1.5 km subset of road, showing an overall intersection-over-union score of 84% for point cloud segmentation and centimetric errors for road inventory parameters. The number of lanes is correctly estimated in 81% of cases. This proposed method offers a safer and more automated approach to road inventory tasks and can be extended to more complex objects and rules for road maintenance and digitalization. The proposed approach has the potential to pave the way for building digital models from as-built infrastructure acquired by mobile mapping systems, making the road inventory process more efficient and accurate. Full article

(This article belongs to the Special Issue Road Detection, Monitoring and Maintenance Using Remotely Sensed Data)

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Review

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44 pages, 7087 KiB

Open AccessReview

Industry- and Academic-Based Trends in Pavement Roughness Inspection Technologies over the Past Five Decades: A Critical Review

by Ali Fares and Tarek Zayed

Remote Sens. 2023, 15(11), 2941; https://doi.org/10.3390/rs15112941 - 5 Jun 2023

Cited by 10 | Viewed by 2493

Abstract

Roughness is widely used as a primary measure of pavement condition. It is also the key indicator of the riding quality and serviceability of roads. The high demand for roughness data has bolstered the evolution of roughness measurement techniques. This study systematically investigated the various trends in pavement roughness measurement techniques within the industry and research community in the past five decades. In this study, the Scopus and TRID databases were utilized. In industry, it was revealed that laser inertial profilers prevailed over response-type methods that were popular until the 1990s. Three-dimensional triangulation is increasingly used in the automated systems developed and used by major vendors in the USA, Canada, and Australia. Among the research community, a boom of research focusing on roughness measurement has been evident in the past few years. The increasing interest in exploring new measurement methods has been fueled by crowdsourcing, the effort to develop cheaper techniques, and the growing demand for collecting roughness data by new industries. The use of crowdsourcing tools, unmanned aerial vehicles (UAVs), and synthetic aperture radar (SAR) images is expected to receive increasing attention from the research community. However, the use of 3D systems is likely to continue gaining momentum in the industry. Full article

(This article belongs to the Special Issue Road Detection, Monitoring and Maintenance Using Remotely Sensed Data)

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Other

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15 pages, 3689 KiB

Open AccessTechnical Note

Long-Range Perception System for Road Boundaries and Objects Detection in Trains

by Wenbo Pan, Xianghua Fan, Hongbo Li and Kai He

Remote Sens. 2023, 15(14), 3473; https://doi.org/10.3390/rs15143473 - 10 Jul 2023

Cited by 3 | Viewed by 1646

Abstract

This article introduces a long-range sensing system based on millimeter-wave radar, which is used to detect the roadside boundaries and track trains for trains. Due to the high speed and long braking distance of trains, existing commercial vehicle sensing solutions cannot meet their needs for long-range target detection. To address this challenge, this study proposes a long-range perception system for detecting road boundaries and trains based on millimeter-wave radar. The system uses high-resolution, long-range millimeter-wave radar customized for the strong scattering environment of rail transit. First, we established a multipath scattering theory in complex scenes such as track tunnels and fences and used the azimuth scattering characteristics to eliminate false detections. A set of accurate calculation methods of the train’s ego-velocity is proposed, which divides the radar detection point clouds into static target point clouds and dynamic target point clouds based on the ego-velocity of the train. We then used the road boundary curvature, global geometric parallel information, and multi-frame information fusion to extract and fit the boundary in the static target point stably. Finally, we performed clustering and shape estimation on the radar track information to identify the train and judge the collision risk based on the position and speed of the detected train and the extracted boundary information. The paper makes a significant contribution by establishing a multipath scattering theory for complex scenes of rail transit to eliminate radar false detection and proposing a train speed estimation strategy and a road boundary feature point extraction method that adapt to the rail environment. As well as building a perception system and installing it on the train for verification, the main line test results showed that the system can reliably detect the road boundary more than 400 m ahead of the train and can stably detect and track the train. Full article

(This article belongs to the Special Issue Road Detection, Monitoring and Maintenance Using Remotely Sensed Data)

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