Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms (Second Edition)
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 31 May 2025 | Viewed by 2234
Special Issue Editors
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
Special Issues, Collections and Topics in MDPI journals
Interests: drones; GPS/GNSS; geomatic; geodesy; photogrammetry; survey; remote sensing; GIS; post seismic survey
Special Issues, Collections and Topics in MDPI journals
Interests: hyperspectral and multispectral imagery; quantitative remote sensing; AI applications; road pavement distress assessment; remote sensing for natural disaster assessment
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
To reach standard safety conditions, numerous Pavement Management Systems are used for pavement assessment, but don’t allow a rapid and synoptic examination for large road networks. Moreover, due to their need to be calculated from in situ surveys, the acquisition of pavement condition indices is expensive and time consuming. Hence, in the last decade Remote Sensing advancements allow to pursue newest automated or semi-automated procedures 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.
Here because the aim of this special issue is to collect research or review papers focusing on innovative approaches on road distress assessment or extraction using spaceborne/aerial (Remote Sensing) and Unmanned Aerial Vehicles (UAVs) (Proximal Sensing) platforms in different experimental surroundings. Additionally, papers focusing on new approaches related to Near Sensing technologies such as Unmanned Ground Vehicles (UGVs) or field spectroscopy, considered preparators’ for RS analysis, are also welcome.
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. Also, geo-statistics can help to improve the knowledge of spectral variability related to pavement distress.
Furthermore, the use of PS techniques shows an increase of their implications on these topics and are frequently related to LIDAR, multi and hyperspectral cameras and field surveys. Such kinds of technologies attain higher outcomes when remote sensed data are correlated to the standardized parameters.
The previous volume of ‘Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms’, was a great success. 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.
Dr. Alessandro Mei
Dr. Valerio Baiocchi
Prof. Dr. Xianfeng Zhang
Guest Editors
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Keywords
- road extraction and pavement distress analysis
- very hight resolution satellite imagery
- airborne remote sensing
- synthetic aperture radar (SAR)
- UAV and UGV
- multispectral and hyperspectral remote sensing
- time series analysis
- change detection
- imaging spectroscopy
- pavement management systems (PMS)
- photogrammetry and 3D modelling
- GIS modelling for management plan
- decision support systems based on remote sensed techniques
- geostatistics
- artificial intelligence
- ML (machine learning)
- CNN
- deep learning
- data fusion
- light detection and ranging (LiDAR)
- pattern recognition
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