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57 pages, 40092 KiB

Open AccessArticle

Surface Motion Prediction and Mapping for Road Infrastructures Management by PS-InSAR Measurements and Machine Learning Algorithms

by Nicholas Fiorentini, Mehdi Maboudi, Pietro Leandri, Massimo Losa and Markus Gerke

Remote Sens. 2020, 12(23), 3976; https://doi.org/10.3390/rs12233976 - 04 Dec 2020

Cited by 26 | Viewed by 4935

Abstract

This paper introduces a methodology for predicting and mapping surface motion beneath road pavement structures caused by environmental factors. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) measurements, geospatial analyses, and Machine Learning Algorithms (MLAs) are employed for achieving the purpose. Two single learners, [...] Read more.

This paper introduces a methodology for predicting and mapping surface motion beneath road pavement structures caused by environmental factors. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) measurements, geospatial analyses, and Machine Learning Algorithms (MLAs) are employed for achieving the purpose. Two single learners, i.e., Regression Tree (RT) and Support Vector Machine (SVM), and two ensemble learners, i.e., Boosted Regression Trees (BRT) and Random Forest (RF) are utilized for estimating the surface motion ratio in terms of mm/year over the Province of Pistoia (Tuscany Region, central Italy, 964 km²), in which strong subsidence phenomena have occurred. The interferometric process of 210 Sentinel-1 images from 2014 to 2019 allows exploiting the average displacements of 52,257 Persistent Scatterers as output targets to predict. A set of 29 environmental-related factors are preprocessed by SAGA-GIS, version 2.3.2, and ESRI ArcGIS, version 10.5, and employed as input features. Once the dataset has been prepared, three wrapper feature selection approaches (backward, forward, and bi-directional) are used for recognizing the set of most relevant features to be used in the modeling. A random splitting of the dataset in 70% and 30% is implemented to identify the training and test set. Through a Bayesian Optimization Algorithm (BOA) and a 10-Fold Cross-Validation (CV), the algorithms are trained and validated. Therefore, the Predictive Performance of MLAs is evaluated and compared by plotting the Taylor Diagram. Outcomes show that SVM and BRT are the most suitable algorithms; in the test phase, BRT has the highest Correlation Coefficient (0.96) and the lowest Root Mean Square Error (0.44 mm/year), while the SVM has the lowest difference between the standard deviation of its predictions (2.05 mm/year) and that of the reference samples (2.09 mm/year). Finally, algorithms are used for mapping surface motion over the study area. We propose three case studies on critical stretches of two-lane rural roads for evaluating the reliability of the procedure. Road authorities could consider the proposed methodology for their monitoring, management, and planning activities. Full article

(This article belongs to the Special Issue Remote Sensing for Transportation Infrastructure Inspection and Monitoring)

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20 pages, 5810 KiB

Open AccessArticle

Neural Network Based Pavement Condition Assessment with Hyperspectral Images

by Okan Bilge Özdemir, Hilal Soydan, Yasemin Yardımcı Çetin and Hafize Şebnem Düzgün

Remote Sens. 2020, 12(23), 3931; https://doi.org/10.3390/rs12233931 - 30 Nov 2020

Cited by 5 | Viewed by 2315

Abstract

Hyperspectral image processing techniques, with their ability to provide information about the chemical compositions of materials, have great potential for pavement condition assessment. This study introduces a novel age-based pavement assessment method, employing an integrated algorithm with artificial neural network (ANN) and spectral [...] Read more.

Hyperspectral image processing techniques, with their ability to provide information about the chemical compositions of materials, have great potential for pavement condition assessment. This study introduces a novel age-based pavement assessment method, employing an integrated algorithm with artificial neural network (ANN) and spectral angle mapping (SAM) on hyperspectral images. In the proposed method, the resulting ANN prediction outputs are used to make a new prediction along with the results from SAM scores. Tests are performed on hyperspectral images that have 360 spectral bands between 400 and 900 nm, collected by a specifically designed vehicular system for proximal image acquisition. The acquired images have eight classes, including three different pavement classes (good (5-year), medium (10-year), and poor (25-year)), yellow dye, white dye, soil, paving stone, and shadow. Several experiments are performed to evaluate the robustness of the followed methodology with limited learning data that include 5, 10, 25, and 50 samples per class, selected randomly from our independent spectral database. For a fair comparison, the individual ANN, SAM, support vector machine (SVM), and stacked auto-encoders (SAE) algorithms are also evaluated. The classification performances of individual ANN and SAM are significantly increased with their joint use, demonstrating a 1.2% to 21% classification accuracy improvement in relation to the training sample size. The study proves that the proposed approach is quite robust in cases wherein few training data are available, while SAE and standard ANN algorithms are more successful in cases wherein more learning data are present. Full article

(This article belongs to the Special Issue Remote Sensing for Transportation Infrastructure Inspection and Monitoring)

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20 pages, 3631 KiB

Open AccessArticle

The Use of Colorimeters to Support Remote Sensing Techniques on Asphalt Pavements

by Christodoulos Mettas, Evagoras Evagorou, Athos Agapiou and Diofantos Hadjimitsis

Remote Sens. 2020, 12(23), 3911; https://doi.org/10.3390/rs12233911 - 28 Nov 2020

Viewed by 2345

Abstract

Characterization of asphalt pavements, based on ground spectroradiometers, has been studied in the past to determine their spectral response concerning the physical, chemical, and condition properties of the pavement. This paper suggests an alternative technique for characterizing ageing of asphalt pavements using a [...] Read more.

Characterization of asphalt pavements, based on ground spectroradiometers, has been studied in the past to determine their spectral response concerning the physical, chemical, and condition properties of the pavement. This paper suggests an alternative technique for characterizing ageing of asphalt pavements using a colorimeter. Colorimeters are considered as affordable equipment in laboratories in contrast to other scientific instruments and turn remote sensing ground techniques more accessible to industry. Therefore, the study proposes a new methodology indicating how colorimeters can be used in combination with satellite data for the age characterization of asphalt pavements. Spectroradiometer data are compared in a two-way methodology to colorimeter data. The final steps of the methodology used in the study show very similar results for both equipment after a comparison of separability indices (Euclidean and Mahalanobis distances). It is a fact that colorimeter data can be used as ground truth data. The application was performed using an in-band analysis of WorldView 3 (WV3) spectral bands situated in the visible electromagnetic spectrum. Based on the findings of this study, we proposed the Normalized Difference Equation/filter for asphalt Pavement Age characterization Index (NDPAI). Full article

(This article belongs to the Special Issue Remote Sensing for Transportation Infrastructure Inspection and Monitoring)

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18 pages, 13670 KiB

Open AccessArticle

GB-SAR in the Diagnosis of Critical City Infrastructure—A Case Study of a Load Test on the Long Tram Extradosed Bridge

by Przemysław Kuras, Łukasz Ortyl, Tomasz Owerko, Marek Salamak and Piotr Łaziński

Remote Sens. 2020, 12(20), 3361; https://doi.org/10.3390/rs12203361 - 15 Oct 2020

Cited by 17 | Viewed by 2410

Abstract

This article describes a case of using remote sensing during a static load test of a large bridge, which, because of its location, belongs to a critical city infrastructure. The bridge in question is the longest tram flyover in Poland. This is an [...] Read more.

This article describes a case of using remote sensing during a static load test of a large bridge, which, because of its location, belongs to a critical city infrastructure. The bridge in question is the longest tram flyover in Poland. This is an extradosed-type concrete structure. It conducts a long tram line over 21 other active lines of an important railway station in the center of Cracow. The diagnostic of such bridges involving the load test method is difficult. Traditional, contact measurements of span displacements are not enough anymore. In such cases, remote sensing becomes an indispensable solution. This publication presents an example of using the close-range radar remote sensing technique of ground-based radar interferometry. However, the cross-sections of the huge bridge were observed using several methods. The aim was to confirm the conditions and efficiency of radar displacement measurements. They were therefore traditional contact measurements using mechanic sensors conducted, if possible, to the bottom of the span, for precise leveling and measurement using electronic total station. Comparing the results as well as the discussion held demonstrated the fundamental advantages of remote sensing methods over the other more traditional techniques. Full article

(This article belongs to the Special Issue Remote Sensing for Transportation Infrastructure Inspection and Monitoring)

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20 pages, 8288 KiB

Open AccessArticle

Pavement Crack Detection from Hyperspectral Images Using a Novel Asphalt Crack Index

by Mohamed Abdellatif, Harriet Peel, Anthony G. Cohn and Raul Fuentes

Remote Sens. 2020, 12(18), 3084; https://doi.org/10.3390/rs12183084 - 20 Sep 2020

Cited by 21 | Viewed by 6877

Abstract

Detection of road pavement cracks is important and needed at an early stage to repair the road and extend its lifetime for maintaining city roads. Cracks are hard to detect from images taken with visible spectrum cameras due to noise and ambiguity with [...] Read more.

Detection of road pavement cracks is important and needed at an early stage to repair the road and extend its lifetime for maintaining city roads. Cracks are hard to detect from images taken with visible spectrum cameras due to noise and ambiguity with background textures besides the lack of distinct features in cracks. Hyperspectral images are sensitive to surface material changes and their potential for road crack detection is explored here. The key observation is that road cracks reveal the interior material that is different from the worn surface material. A novel asphalt crack index is introduced here as an additional clue that is sensitive to the spectra in the range 450–550 nm. The crack index is computed and found to be strongly correlated with the appearance of fresh asphalt cracks. The new index is then used to differentiate cracks from road surfaces. Several experiments have been made, which confirmed that the proposed index is effective for crack detection. The recall-precision analysis showed an increase in the associated F1-score by an average of 21.37% compared to the VIS2 metric in the literature (a metric used to classify pavement condition from hyperspectral data). Full article

(This article belongs to the Special Issue Remote Sensing for Transportation Infrastructure Inspection and Monitoring)

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24 pages, 12466 KiB

Open AccessArticle

Verification of GNSS Measurements of the Railway Track Using Standard Techniques for Determining Coordinates

by Cezary Specht, Andrzej Wilk, Wladyslaw Koc, Krzysztof Karwowski, Paweł Dąbrowski, Mariusz Specht, Sławomir Grulkowski, Piotr Chrostowski, Jacek Szmagliński, Krzysztof Czaplewski, Jacek Skibicki, Slawomir Judek and Roksana Licow

Remote Sens. 2020, 12(18), 2874; https://doi.org/10.3390/rs12182874 - 04 Sep 2020

Cited by 10 | Viewed by 3320

Abstract

The problem of the reproduction of the railway geometric layout in the global spatial system is currently solved in the form of measurements that use geodetic railway networks and also, in recent years, efficient methods of mobile positioning (mainly satellite and inert). The [...] Read more.

The problem of the reproduction of the railway geometric layout in the global spatial system is currently solved in the form of measurements that use geodetic railway networks and also, in recent years, efficient methods of mobile positioning (mainly satellite and inert). The team of authors from the Gdańsk University of Technology and the Maritime University in Gdynia as part of the research project InnoSatTrack is looking for effective and efficient methods for the inventory of railway lines. The research is part of a wider investigation BRIK (Research and Development in Railway Infrastructure, in polish: Badania i Rozwój w Infrastrukturze Kolejowej). This paper presents a comparative analysis of the problem of the reproduction of the trajectory of the measuring system using tacheometry, satellite measurements made using a measurement trolley, and mobile satellite measurements. Algorithms enabling the assessment of the compliance of satellite measurements with classic tacheometric measurements were presented. To this end, the authors held measurement sessions using modern geodetic instruments and satellite navigation on a section of the railway line. The results of the measurements indicate the convergence of the level of accuracy achieved by different measuring techniques. Full article

(This article belongs to the Special Issue Remote Sensing for Transportation Infrastructure Inspection and Monitoring)

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12 pages, 9108 KiB

Open AccessLetter

A Study of Sonar Image Stabilization of Unmanned Surface Vehicle Based on Motion Sensor for Inspection of Underwater Infrastructure

by Youngseok Kim and Jaesuk Ryou

Remote Sens. 2020, 12(21), 3481; https://doi.org/10.3390/rs12213481 - 23 Oct 2020

Cited by 7 | Viewed by 2953

Abstract

In order to detect damage to underwater infrastructure for inspection, an expensive survey by a diver is generally conducted, but it carries the risk of accidents. Accordingly, the development of an effective unmanned underwater survey system is an important priority. The unmanned underwater [...] Read more.

In order to detect damage to underwater infrastructure for inspection, an expensive survey by a diver is generally conducted, but it carries the risk of accidents. Accordingly, the development of an effective unmanned underwater survey system is an important priority. The unmanned underwater survey system used in this study is equipped with sonar towed by an- Unmanned Surface Vehicle (USV) to conduct the survey, but the USV is more affected by the waves and swells than a common boat. As a result, distorted sonar data causes errors in the information regarding the damage of underwater infrastructure. This study proposes the method of sonar image stabilization to minimize the errors of the distortion of sonar data by using a motion sensor. The change in the amount of the roll was calculated from the motion sensor, and the sonar data was corrected in the sonar ping unit. The sonar image stabilization algorithm was verified through field tests, and the error rate before and after correction was reduced by 15%. It is expected that, in the future, the proposed approach will be used as a standard data-gathering system for securing the reliability of sonar data when performing an unmanned underwater survey. Full article

(This article belongs to the Special Issue Remote Sensing for Transportation Infrastructure Inspection and Monitoring)

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17 pages, 5575 KiB

Open AccessTechnical Note

A Case Study on the Noncontact Inventory of the Oldest European Cast-iron Bridge Using Terrestrial Laser Scanning and Photogrammetric Techniques

by Jacek Kwiatkowski, Wojciech Anigacz and Damian Beben

Remote Sens. 2020, 12(17), 2745; https://doi.org/10.3390/rs12172745 - 25 Aug 2020

Cited by 16 | Viewed by 3167

Abstract

Conventional measurement technologies of transportation infrastructures consist of discrete surveys which can be inconvenient in practice. Furthermore, data obtained using these methods are restricted to several points (or elements) placed on the observed structures. Modern survey techniques—for example, terrestrial laser scanning (TLS) and [...] Read more.

Conventional measurement technologies of transportation infrastructures consist of discrete surveys which can be inconvenient in practice. Furthermore, data obtained using these methods are restricted to several points (or elements) placed on the observed structures. Modern survey techniques—for example, terrestrial laser scanning (TLS) and photogrammetric—allow for the surveying of quasi-continuous surfaces of examined structures. The examined object is an historic cast-iron suspension bridge in Ozimek (south of Poland). The bridge was constructed in 1825–1827 and constitutes the oldest European bridge of this type. The surveys were conducted using TLS and digital photogrammetric techniques. The data obtained were compared with traditional survey results (reference data) and the project. The achieved effects of the measurements show that the discrepancies between the applied techniques (TLS and photogrammetry) and reference methods varied only within several millimeters and can be regarded as satisfactory. Better compliance was obtained for TLS than photogrammetry. The main benefits of the applied techniques include reducing time in the field and obtaining a three-dimensional model of the structure that has satisfactory accuracy. Full article

(This article belongs to the Special Issue Remote Sensing for Transportation Infrastructure Inspection and Monitoring)

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