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Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 32955

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Special Issue Editors

Dr. Alessandro Mei

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Institute of Atmospheric Pollution Research (CNR-IIA), National Research Council of Italy, Monterotondo, RM, Italy
Interests: UAV; aircraft and satellite remote sensing; multispectral and hyperspectral remote sensing; imaging spectroscopy; asphalt pavement analysis by remote sensing techniques; analysis of bituminous mixtures by digital imaging processing; characterization of traditional and bio-plastics by hyperspectral devices; photogrammetry and 3D modelling; GIS and geospatial statistics; calibration/validation; land use land cover change; downscaling techniques
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Prof. Dr. Xianfeng Zhang

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Institute of Remote Sensing and Geographic Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China
Interests: hyperspectral and multispectral imagery; quantitative remote sensing; AI applications; road pavement distress assessment; remote sensing for natural disaster assessment
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Dr. Valerio Baiocchi

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Department of Civil, Constructional and Environmental Engineering, Sapienza University of Rome, I-00184 Rome, Italy
Interests: drones; GPS/GNSS; geomatic; geodesy; photogrammetry; survey; remote sensing; GIS; post seismic survey
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Dear Colleagues,

As known, road maintenance has a deep impact on authorities' financial plans. Currently, to reach standard safety conditions, numerous PMS systems and indicators are used for pavement assessment such as the Pavement Condition Index (PCI), or the Structure Index (SI) but, both don’t allow a rapid synoptic pavement investigation for large road networks. Moreover, due to their need to be calculated from in situ surveys, the acquisition of such indices is expensive and time consuming. Hence, in the last decade the advancement of automated or semi-automated procedures is stimulated for pavement distress detection and analysis. Here because, a great interest has grown-up in the scientific community to the adoption of remote sensed non-invasive techniques in several experimental settings.

Remote sensing represents an interesting alternative and challenge for road extraction and pavement aging condition monitoring by using both passive and active satellite sensors. Moreover, the increase in the adoption of Artificial Intelligence (AI) and Big Data based on remote sensing allows us to manage and share in a more efficient way such huge data frames.

Furthermore, the use of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) show an increase of their implications on these topics and are frequently related to field surveys. Such kinds of technologies attain higher outcomes when remote sensed data are correlated to the standardized parameters.

The aim of this special issue is to collect research or review papers focusing on innovative and multidisciplinary approaches on road extraction or distress assessment using spaceborne, aerial and terrestrial platforms in different experimental surroundings. Additionally, papers focusing on new field approaches related to spectroscopy, photogrammetry, GPR, LIDAR, laser scanners, etc. are also welcome.

Dr. Alessandro Mei
Prof. Dr. Xianfeng Zhang
Prof. Dr. Valerio Baiocchi
Guest Editors

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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.

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Keywords

road extraction and pavement distress analysis
spaceborne, airborne and terrestrial platforms
UAV and UGV
multispectral and hyperspectral Remote Sensing
time series analysis and change detection
imaging spectroscopy
Pavement Condition Index, Structure Index, Serviceability Index
Pavement Management systems (PMS)
bituminous mixtures analyses by non-invasive techniques
photogrammetry and 3D modelling
GIS modelling for management plan
Decision Support Systems based on remote sensed techniques
geomatics
AI
deep learning
data fusion
Ground Penetrating Radar (GPR)
Accelerated Pavement Tests (APT)
lidar and laser scanning
geometric reconstruction
pattern recognition

Related Special Issue

Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms (Second Edition) in Remote Sensing

Published Papers (13 papers)

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Editorial

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6 pages, 182 KiB

Open AccessEditorial

Road Extraction and Distress Assessment by Spaceborne, Airborne, and Terrestrial Platforms

by Valerio Baiocchi, Xianfeng Zhang and Alessandro Mei

Remote Sens. 2024, 16(8), 1416; https://doi.org/10.3390/rs16081416 - 17 Apr 2024

Viewed by 298

Abstract

The road systems connecting villages, cities, and countries stand as a pivotal transportation infrastructure in modern society [...] Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms)

Research

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21 pages, 7540 KiB

Open AccessArticle

Assessment of Pavement Structural Conditions and Remaining Life Combining Accelerated Pavement Testing and Ground-Penetrating Radar

by Zhen Liu, Qifeng Yang and Xingyu Gu

Remote Sens. 2023, 15(18), 4620; https://doi.org/10.3390/rs15184620 - 20 Sep 2023

Cited by 10 | Viewed by 1169

Abstract

The inspection and monitoring of structural conditions are crucial for the maintenance of semi-rigid base pavement. To achieve the inverse calculation of material parameters and obtain the mechanical response of asphalt pavement, a method of modulus correction by reducing the error between tested and simulated strains was first developed. The relationship between the temperature at various depths within the pavement structure and atmospheric temperature was effectively demonstrated using a dual sinusoidal regression model. Subsequently, pavement monitoring data illustrated that as loading weight and temperature increased and loading speed decreased, the three-way strain of the asphalt layer increased. Thus, the relationship model between loading conditions and three-way strain was established with a good fitting degree (R² > 0.95). The corrected modulus was obtained by approximating the error between simulated and measured strains. Then, the finite element analysis was performed to calculate key mechanical index values under various working conditions and predict the fatigue life of asphalt and base layers. Finally, ground-penetrating radar (GPR) detection was performed, and the internal pavement condition index was defined for quantitative assessment of structure conditions. The results show that there is a good correlation between the internal pavement condition index (IPCI) and remaining life of pavement structure. Therefore, our works solve the problems of the parameter reliability of pavement structures and quantitative assessment for structural conditions, which could support the performance prediction and maintenance analysis on asphalt pavement with a semi-rigid base. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms)

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13 pages, 7525 KiB

Open AccessCommunication

LDANet: A Lightweight Dynamic Addition Network for Rural Road Extraction from Remote Sensing Images

by Bohua Liu, Jianli Ding, Jie Zou, Jinjie Wang and Shuai Huang

Remote Sens. 2023, 15(7), 1829; https://doi.org/10.3390/rs15071829 - 29 Mar 2023

Cited by 7 | Viewed by 1639

Abstract

Automatic road extraction from remote sensing images has an important impact on road maintenance and land management. While significant deep-learning-based approaches have been developed in recent years, achieving a suitable trade-off between extraction accuracy, inference speed and model size remains a fundamental and challenging issue for real-time road extraction applications, especially for rural roads. For this purpose, we developed a lightweight dynamic addition network (LDANet) to exploit rural road extraction. Specifically, considering the narrow, complex and diverse nature of rural roads, we introduce an improved Asymmetric Convolution Block (ACB)-based Inception structure to extend the low-level features in the feature extraction layer. In the deep feature association module, the depth-wise separable convolution (DSC) is introduced to reduce the computational complexity of the model, and an adaptation-weighted overlay is designed to capture the salient features. Moreover, we utilize a dynamic weighted combined loss, which can better solve the sample imbalance and boosts segmentation accuracy. In addition, we constructed a typical remote sensing dataset of rural roads based on the Deep Globe Land Cover Classification Challenge dataset. Our experiments demonstrate that LDANet performs well in road extraction with fewer model parameters (<1 MB) and that the accuracy and the mean Intersection over Union reach 98.74% and 76.21% on the test dataset, respectively. Therefore, LDANet has potential to rapidly extract and monitor rural roads from remote sensing images. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms)

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25 pages, 17665 KiB

Open AccessArticle

ISTD-PDS7: A Benchmark Dataset for Multi-Type Pavement Distress Segmentation from CCD Images in Complex Scenarios

by Weidong Song, Zaiyan Zhang, Bing Zhang, Guohui Jia, Hongbo Zhu and Jinhe Zhang

Remote Sens. 2023, 15(7), 1750; https://doi.org/10.3390/rs15071750 - 24 Mar 2023

Cited by 1 | Viewed by 2632

Abstract

The lack of large-scale, multi-scene, and multi-type pavement distress training data reduces the generalization ability of deep learning models in complex scenes, and limits the development of pavement distress extraction algorithms. Thus, we built the first large-scale dichotomous image segmentation (DIS) dataset for multi-type pavement distress segmentation, called ISTD-PDS7, aimed to segment highly accurate pavement distress types from natural charge-coupled device (CCD) images. The new dataset covers seven types of pavement distress in nine types of scenarios, along with negative samples with texture similarity noise. The final dataset contains 18,527 images, which is many more than the previously released benchmarks. All the images are annotated with fine-grained labels. In addition, we conducted a large benchmark test, evaluating seven state-of-the-art segmentation models, providing a detailed discussion of the factors that influence segmentation performance, and making cross-dataset evaluations for the best-performing model. Finally, we investigated the effectiveness of negative samples in reducing false positive prediction in complex scenes and developed two potential data augmentation methods for improving the segmentation accuracy. We hope that these efforts will create promising developments for both academics and the industry. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms)

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21 pages, 14587 KiB

Open AccessArticle

A Low-Cost Deep Learning System to Characterize Asphalt Surface Deterioration

by Diogo Inácio, Henrique Oliveira, Pedro Oliveira and Paulo Correia

Remote Sens. 2023, 15(6), 1701; https://doi.org/10.3390/rs15061701 - 22 Mar 2023

Viewed by 1526

Abstract

Every day millions of people travel on highways for work- or leisure-related purposes. Ensuring road safety is thus of paramount importance, and maintaining good-quality road pavements is essential, requiring an effective maintenance policy. The automation of some road pavement maintenance tasks can reduce the time and effort required from experts. This paper proposes a simple system to help speed up road pavement surface inspection and its analysis towards making maintenance decisions. A low-cost video camera mounted on a vehicle was used to capture pavement imagery, which was fed to an automatic crack detection and classification system based on deep neural networks. The system provided two types of output: (i) a cracking percentage per road segment, providing an alert to areas that require attention from the experts; (ii) a segmentation map highlighting which areas of the road pavement surface are affected by cracking. With this data, it became possible to select which maintenance or rehabilitation processes the road pavement required. The system achieved promising results in the analysis of highway pavements, and being automated and having a low processing time, the system is expected to be an effective aid for experts dealing with road pavement maintenance. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms)

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27 pages, 9130 KiB

Open AccessArticle

Road Condition Detection and Emergency Rescue Recognition Using On-Board UAV in the Wildness

by Chang Liu and Tamás Szirányi

Remote Sens. 2022, 14(17), 4355; https://doi.org/10.3390/rs14174355 - 02 Sep 2022

Cited by 6 | Viewed by 3260

Abstract

Unmanned aerial vehicle (UAV) vision technology is becoming increasingly important, especially in wilderness rescue. For humans in the wilderness with poor network conditions and bad weather, this paper proposes a technique for road extraction and road condition detection from video captured by UAV multispectral cameras in real-time or pre-downloaded multispectral images from satellites, which in turn provides humans with optimal route planning. Additionally, depending on the flight altitude of the UAV, humans can interact with the UAV through dynamic gesture recognition to identify emergency situations and potential dangers for emergency rescue or re-routing. The purpose of this work is to detect the road condition and identify emergency situations in order to provide necessary and timely assistance to humans in the wild. By obtaining a normalized difference vegetation index (NDVI), the UAV can effectively distinguish between bare soil roads and gravel roads, refining the results of our previous route planning data. In the low-altitude human–machine interaction part, based on media-pipe hand landmarks, we combined machine learning methods to build a dataset of four basic hand gestures for sign for help dynamic gesture recognition. We tested the dataset on different classifiers, and the best results show that the model can achieve 99.99% accuracy on the testing set. In this proof-of-concept paper, the above experimental results confirm that our proposed scheme can achieve our expected tasks of UAV rescue and route planning. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms)

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18 pages, 90111 KiB

Open AccessArticle

Automatic Detection of Pothole Distress in Asphalt Pavement Using Improved Convolutional Neural Networks

by Danyu Wang, Zhen Liu, Xingyu Gu, Wenxiu Wu, Yihan Chen and Lutai Wang

Remote Sens. 2022, 14(16), 3892; https://doi.org/10.3390/rs14163892 - 11 Aug 2022

Cited by 47 | Viewed by 3329

Abstract

To realize the intelligent and accurate measurement of pavement surface potholes, an improved You Only Look Once version three (YOLOv3) object detection model combining data augmentation and structure optimization is proposed in this study. First, color adjustment was used to enhance the image contrast, and data augmentation was performed through geometric transformation. Pothole categories were subdivided into P1 and P2 on the basis of whether or not there was water. Then, the Residual Network (ResNet101) and complete IoU (CIoU) loss were used to optimize the structure of the YOLOv3 model, and the K-Means++ algorithm was used to cluster and modify the multiscale anchor sizes. Lastly, the robustness of the proposed model was assessed by generating adversarial examples. Experimental results demonstrated that the proposed model was significantly improved compared with the original YOLOv3 model; the detection mean average precision (mAP) was 89.3%, and the F1-score was 86.5%. On the attacked testing dataset, the overall mAP value reached 81.2% (−8.1%), which shows that this proposed model performed well on samples after random occlusion and adding noise interference, proving good robustness. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms)

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20 pages, 13553 KiB

Open AccessArticle

ASFF-YOLOv5: Multielement Detection Method for Road Traffic in UAV Images Based on Multiscale Feature Fusion

by Mulan Qiu, Liang Huang and Bo-Hui Tang

Remote Sens. 2022, 14(14), 3498; https://doi.org/10.3390/rs14143498 - 21 Jul 2022

Cited by 25 | Viewed by 4294

Abstract

Road traffic elements are important components of roads and the main elements of structuring basic traffic geographic information databases. However, the following problems still exist in the detection and recognition of road traffic elements: dense elements, poor detection effect of multi-scale objects, and small objects being easily affected by occlusion factors. Therefore, an adaptive spatial feature fusion (ASFF) YOLOv5 network (ASFF-YOLOv5) was proposed for the automatic recognition and detection of multiple multiscale road traffic elements. First, the K-means++ algorithm was used to make clustering statistics on the range of multiscale road traffic elements, and the size of the candidate box suitable for the dataset was obtained. Then, a spatial pyramid pooling fast (SPPF) structure was used to improve the classification accuracy and speed while achieving richer feature information extraction. An ASFF strategy based on a receptive field block (RFB) was proposed to improve the feature scale invariance and enhance the detection effect of small objects. Finally, the experimental effect was evaluated by calculating the mean average precision (mAP). Experimental results showed that the mAP value of the proposed method was 93.1%, which is 19.2% higher than that of the original YOLOv5 model. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms)

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19 pages, 3331 KiB

Open AccessArticle

A Recurrent Adaptive Network: Balanced Learning for Road Crack Segmentation with High-Resolution Images

by Yi Zhang, Junfu Fan, Mengzhen Zhang, Zongwen Shi, Rufei Liu and Bing Guo

Remote Sens. 2022, 14(14), 3275; https://doi.org/10.3390/rs14143275 - 07 Jul 2022

Cited by 5 | Viewed by 2069

Abstract

Road crack segmentation based on high-resolution images is an important task in road service maintenance. The undamaged road surface area is much larger than the damaged area on a highway. This imbalanced situation yields poor road crack segmentation performance for convolutional neural networks. In this paper, we first evaluate the mainstream convolutional neural network structure in the road crack segmentation task. Second, inspired by the second law of thermodynamics, an improved method called a recurrent adaptive network for a pixelwise road crack segmentation task is proposed to solve the extreme imbalance between positive and negative samples. We achieved a flow between precision and recall, similar to the conduction of temperature repetition. During the training process, the recurrent adaptive network (1) dynamically evaluates the degree of imbalance, (2) determines the positive and negative sampling rates, and (3) adjusts the loss weights of positive and negative features. By following these steps, we established a channel between precision and recall and kept them balanced as they flow to each other. A dataset of high-resolution road crack images with annotations (named HRRC) was built from a real road inspection scene. The images in HRRC were collected on a mobile vehicle measurement platform by high-resolution industrial cameras and were carefully labeled at the pixel level. Therefore, this dataset has sufficient data complexity to objectively evaluate the real performance of convolutional neural networks in highway patrol scenes. Our main contribution is a new method of solving the data imbalance problem, and the method of guiding model training by analyzing precision and recall is experimentally demonstrated to be effective. The recurrent adaptive network achieves state-of-the-art performance on this dataset. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms)

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21 pages, 19246 KiB

Open AccessArticle

Damage Properties of the Block-Stone Embankment in the Qinghai–Tibet Highway Using Ground-Penetrating Radar Imagery

by Shunshun Qi, Guoyu Li, Dun Chen, Mingtang Chai, Yu Zhou, Qingsong Du, Yapeng Cao, Liyun Tang and Hailiang Jia

Remote Sens. 2022, 14(12), 2950; https://doi.org/10.3390/rs14122950 - 20 Jun 2022

Cited by 7 | Viewed by 3766

Abstract

The block-stone embankment is a special type of embankment widely used to protect the stability of the underlying warm and ice-rich permafrost. Under the influence of multiple factors, certain damages will still occur in the block-stone embankment after a period of operation, which may weaken or destroy its cooling function, introducing more serious damages to the Qinghai–Tibet Highway (QTH). Ground-penetrating radar (GPR), a nondestructive testing technique, was adopted to investigate the damage properties of the damaged block-stone embankment. GPR imagery, together with the other data and methods (structural characteristics, field survey data, GPR parameters, etc.), indicated four categories of damage: (i) loosening of the upper sand-gravel layer; (ii) loosening of the block-stone layer; (iii) settlement of the block-stone layer; and (iv) dense filling of the block-stones layer. The first two conditions were widely distributed, whereas the settlement and dense filling of the block-stone layer were less so, and the other combined damages also occurred frequently. The close correlation between the different damages indicated a causal relationship. A preliminary discussion of these observations about the influences on the formation of the damage of the block-stone embankment is included. The findings provide some points of reference for the future construction and maintenance of block-stone embankments in permafrost regions. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms)

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20 pages, 12999 KiB

Open AccessArticle

A Fast Inference Vision Transformer for Automatic Pavement Image Classification and Its Visual Interpretation Method

by Yihan Chen, Xingyu Gu, Zhen Liu and Jia Liang

Remote Sens. 2022, 14(8), 1877; https://doi.org/10.3390/rs14081877 - 13 Apr 2022

Cited by 14 | Viewed by 3278

Abstract

Traditional automatic pavement distress detection methods using convolutional neural networks (CNNs) require a great deal of time and resources for computing and are poor in terms of interpretability. Therefore, inspired by the successful application of Transformer architecture in natural language processing (NLP) tasks, a novel Transformer method called LeViT was introduced for automatic asphalt pavement image classification. LeViT consists of convolutional layers, transformer stages where Multi-layer Perception (MLP) and multi-head self-attention blocks alternate using the residual connection, and two classifier heads. To conduct the proposed methods, three different sources of pavement image datasets and pre-trained weights based on ImageNet were attained. The performance of the proposed model was compared with six state-of-the-art (SOTA) deep learning models. All of them were trained based on transfer learning strategy. Compared to the tested SOTA methods, LeViT has less than 1/8 of the parameters of the original Vision Transformer (ViT) and 1/2 of ResNet and InceptionNet. Experimental results show that after training for 100 epochs with a 16 batch-size, the proposed method acquired 91.56% accuracy, 91.72% precision, 91.56% recall, and 91.45% F1-score in the Chinese asphalt pavement dataset and 99.17% accuracy, 99.19% precision, 99.17% recall, and 99.17% F1-score in the German asphalt pavement dataset, which is the best performance among all the tested SOTA models. Moreover, it shows superiority in inference speed (86 ms/step), which is approximately 25% of the original ViT method and 80% of some prevailing CNN-based models, including DenseNet, VGG, and ResNet. Overall, the proposed method can achieve competitive performance with fewer computation costs. In addition, a visualization method combining Grad-CAM and Attention Rollout was proposed to analyze the classification results and explore what has been learned in every MLP and attention block of LeViT, which improved the interpretability of the proposed pavement image classification model. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms)

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15 pages, 10468 KiB

Open AccessTechnical Note

Fast Segmentation and Dynamic Monitoring of Time-Lapse 3D GPR Data Based on U-Net

by Ke Shang, Feizhou Zhang, Ao Song, Jianyu Ling, Jiwen Xiao, Zihan Zhang and Rongyi Qian

Remote Sens. 2022, 14(17), 4190; https://doi.org/10.3390/rs14174190 - 25 Aug 2022

Cited by 2 | Viewed by 1592

Abstract

As the amount of ground-penetrating radar (GPR) data increases significantly with the high demands of nondestructive detection methods under urban roads, a method suitable for time-lapse data dynamic monitoring should be developed to quickly identify targets on GPR profiles and compare time-lapse datasets. This study conducted a field experiment aiming to monitor one backfill pit using three-dimensional GPR (3D GPR), and the time-lapse data collected over four months were used to train U-Net, a fast neural network based on convolutional neural networks (CNNs). Consequently, a trained network model that could effectively segment the backfill pit from inline profiles was obtained, whose Intersection over Union (IoU) was 0.83 on the test dataset. Moreover, segmentation masks were compared, demonstrating that a change in the southwest side of the backfill pit may exist. The results demonstrate the potential of machine learning algorithms in time-lapse 3D GPR data segmentation and dynamic monitoring. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms)

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13 pages, 4383 KiB

Open AccessTechnical Note

Research on the Dynamic Monitoring Technology of Road Subgrades with Time-Lapse Full-Coverage 3D Ground Penetrating Radar (GPR)

by Jianyu Ling, Rongyi Qian, Ke Shang, Linyan Guo, Yu Zhao and Dongyi Liu

Remote Sens. 2022, 14(7), 1593; https://doi.org/10.3390/rs14071593 - 26 Mar 2022

Cited by 10 | Viewed by 2272

Abstract

Road safety is important for the rapid development of the economy and society. Thus, it is of great significance to monitor the dynamic changing processes of road diseases, such as cavities, to provide a basis for the daily maintenance of roads and prevent any possible car accidents. The ground penetrating radar (GPR) technology is widely used in road disease detection due to its advantages of nondestructiveness, rapidness, and high resolution. Traditionally, one-time 2D GPR detection cannot obtain the 3D spatial changes of subgrades. Thus, we developed a road subgrade monitoring method based on the time-lapse full-coverage (TLFC) 3D GPR technique by focusing on solving the key problems of time and spatial position mismatches in experimental data. Moreover, we used the time zero consistency correction, 3D data combination, and spatial position matching methods, as they greatly improve the 3D imaging quality of underground spaces. Finally, the time-lapse attribute analysis method was used in the TLFC 3D GPR data to obtain detailed characteristics and an overall rule of the dynamic subgrade change. Overall, this research proves that TLFC 3D GPR is an optimal choice for road subgrade monitoring. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms)

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