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

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 561

Special Issue Editors


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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

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Pisa, Largo L. Lazzarino, 1, 56122 Pisa, Italy
Interests: road safety; accident data analysis; surface pavement characteristics; tyre pavement interaction; road monitoring
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 by roads; they offer a critical contribution to economic development and help 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. Addressing these challenges is particularly difficult for road authorities with limited resources, especially for managing local and minor road networks. This highlights the urgent need for innovative and effective road detection, monitoring and maintenance strategies.

Building on the success of our first Special Issue (https://www.mdpi.com/journal/remotesensing/special_issues/Road_Detection), “Road Detection, Monitoring and Maintenance Using Remotely Sensed Data (2nd Edition)” continues to explore cutting-edge advancements in this field. This follow-up Issue seeks to further develop and disseminate innovative solutions that leverage remotely sensed data, non-destructive testing (NDT) methods and artificial intelligence (AI) to enhance the planning, monitoring and maintenance of road infrastructures.

Indeed, the integration of NDT surveys, when combined with AI-based algorithms, have emerged as indispensable tools for managing road networks. These approaches enable road authorities to accurately identify the causes and consequences of infrastructure distress, such as structural deficiencies or damage caused by environmental and operational factors. The integration of multiple NDT techniques provides a comprehensive perspective on the functional and structural conditions of roads, paving the way for optimized decision-making and resource allocation.

This Special Issue invites submissions addressing the latest developments in these areas, including innovative methodologies, applications and case studies. The aim here is to further support road authorities and researchers in identifying the most suitable remote surveys for various types of infrastructure, integrating these surveys with ancillary data, and optimizing maintenance strategies using AI.

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

  • Monitoring of road networks by remote techniques (satellite-, aerial-, ground-, and subsoil-based non-destructive techniques);
  • Integration of remotely sensed data in pavement monitoring and maintenance activities;
  • 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 technique data in road detection, road monitoring, and road maintenance;
  • Digital twins for pavement management systems.

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

Dr. Nicholas Fiorentini
Prof. Massimo Losa
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

  • 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 vehicle (UAV) imagery
  • pavement management systems (PMSs)
  • artificial intelligence (AI)
  • decision support systems (DSS)
  • data integration and data fusion

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Published Papers (1 paper)

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Research

18 pages, 5809 KiB  
Article
UAV-Based Quantitative Assessment of Road Embankment Smoothness and Compaction Using Curvature Analysis and Intelligent Monitoring
by Jin-Young Kim, Jin-Woo Cho, Chang-Ho Choi and Sung-Yeol Lee
Remote Sens. 2025, 17(11), 1867; https://doi.org/10.3390/rs17111867 - 27 May 2025
Viewed by 262
Abstract
Smart construction technology integrates artificial intelligence, Internet of Things, UAVs, and building information modeling to improve productivity and quality in construction. In road embankment earthworks, ground compaction quality is critical for structural stability and maintenance. This study proposes a methodology combining UAV photogrammetry [...] Read more.
Smart construction technology integrates artificial intelligence, Internet of Things, UAVs, and building information modeling to improve productivity and quality in construction. In road embankment earthworks, ground compaction quality is critical for structural stability and maintenance. This study proposes a methodology combining UAV photogrammetry with intelligent compaction quality management systems to evaluate surface flatness and compaction homogeneity in real-time. High-resolution UAV images were used to generate digital elevation models, from which surface roughness was extracted using terrain element analysis and fast Fourier transform. Local terrain changes were interpreted through contour gradient, outline gradient, and tangential gradient curvature analysis. Field tests were conducted at a pilot site using a vibratory roller, followed by four compaction quality assessments: plate load test, dynamic cone penetration test, light falling weight deflectometer, and compaction meter value. UAV-based flatness analysis revealed that, when surface flatness met the standard, a strong correlation was observed, with results from conventional field tests and intelligent compaction data. The proposed method effectively identified poorly compacted zones and spatial inhomogeneity without interrupting construction. These findings demonstrate that UAV-based terrain analysis can serve as a nondestructive real-time monitoring tool and contribute to automated quality control in smart construction environments. Full article
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