Road Detection, Monitoring, and Maintenance Using Remotely Sensed Data
Roads are a form of critical infrastructure, influencing economic growth, mobility, and public safety. However, the management, monitoring, and maintenance of road networks remain a challenge, particularly given limited budgets and the complexity of assessing widespread infrastructure. This Special...
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MDPI AG
2025-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/5/917 |
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| author | Nicholas Fiorentini Massimo Losa |
| author_facet | Nicholas Fiorentini Massimo Losa |
| author_sort | Nicholas Fiorentini |
| collection | DOAJ |
| description | Roads are a form of critical infrastructure, influencing economic growth, mobility, and public safety. However, the management, monitoring, and maintenance of road networks remain a challenge, particularly given limited budgets and the complexity of assessing widespread infrastructure. This Special Issue on “Road Detection, Monitoring, and Maintenance Using Remotely Sensed Data” presents innovative strategies leveraging remote sensing technologies, artificial intelligence (AI), and non-destructive testing (NDT) to optimize road infrastructure assessment. The ten papers published in this issue explore diverse methodologies, including novel deep learning algorithms for road inventory, novel methods for pavement crack detection, AI-enhanced ground-penetrating radar (GPR) imaging for subsurface assessment, high-resolution optical satellite imagery for unpaved road assessment, and aerial orthophotography for road mapping. Collectively, these studies demonstrate the transformative potential of remotely sensed data for improving the efficiency, accuracy, and scalability of road monitoring and maintenance processes. The findings highlight the importance of integrating multi-source remote sensing data with advanced AI-based techniques to develop cost-effective, automated, and scalable solutions for road authorities. As the first edition of this Special Issue, these contributions lay the groundwork for future advancements in remote sensing applications for road network management. |
| format | Article |
| id | doaj-art-9f7550534da24aedad8aed5f7de882b9 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-9f7550534da24aedad8aed5f7de882b92025-08-20T02:59:07ZengMDPI AGRemote Sensing2072-42922025-03-0117591710.3390/rs17050917Road Detection, Monitoring, and Maintenance Using Remotely Sensed DataNicholas Fiorentini0Massimo Losa1Department of Civil and Industrial Engineering (DICI), The University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, ItalyDepartment of Civil and Industrial Engineering (DICI), The University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, ItalyRoads are a form of critical infrastructure, influencing economic growth, mobility, and public safety. However, the management, monitoring, and maintenance of road networks remain a challenge, particularly given limited budgets and the complexity of assessing widespread infrastructure. This Special Issue on “Road Detection, Monitoring, and Maintenance Using Remotely Sensed Data” presents innovative strategies leveraging remote sensing technologies, artificial intelligence (AI), and non-destructive testing (NDT) to optimize road infrastructure assessment. The ten papers published in this issue explore diverse methodologies, including novel deep learning algorithms for road inventory, novel methods for pavement crack detection, AI-enhanced ground-penetrating radar (GPR) imaging for subsurface assessment, high-resolution optical satellite imagery for unpaved road assessment, and aerial orthophotography for road mapping. Collectively, these studies demonstrate the transformative potential of remotely sensed data for improving the efficiency, accuracy, and scalability of road monitoring and maintenance processes. The findings highlight the importance of integrating multi-source remote sensing data with advanced AI-based techniques to develop cost-effective, automated, and scalable solutions for road authorities. As the first edition of this Special Issue, these contributions lay the groundwork for future advancements in remote sensing applications for road network management.https://www.mdpi.com/2072-4292/17/5/917road detectionroad monitoringroad maintenanceremotely sensed datanon-destructive techniquesdeep learning |
| spellingShingle | Nicholas Fiorentini Massimo Losa Road Detection, Monitoring, and Maintenance Using Remotely Sensed Data Remote Sensing road detection road monitoring road maintenance remotely sensed data non-destructive techniques deep learning |
| title | Road Detection, Monitoring, and Maintenance Using Remotely Sensed Data |
| title_full | Road Detection, Monitoring, and Maintenance Using Remotely Sensed Data |
| title_fullStr | Road Detection, Monitoring, and Maintenance Using Remotely Sensed Data |
| title_full_unstemmed | Road Detection, Monitoring, and Maintenance Using Remotely Sensed Data |
| title_short | Road Detection, Monitoring, and Maintenance Using Remotely Sensed Data |
| title_sort | road detection monitoring and maintenance using remotely sensed data |
| topic | road detection road monitoring road maintenance remotely sensed data non-destructive techniques deep learning |
| url | https://www.mdpi.com/2072-4292/17/5/917 |
| work_keys_str_mv | AT nicholasfiorentini roaddetectionmonitoringandmaintenanceusingremotelysenseddata AT massimolosa roaddetectionmonitoringandmaintenanceusingremotelysenseddata |