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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: 24 September 2024 | Viewed by 361

Special Issue Editors


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Guest Editor
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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Guest Editor
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
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
Prof. Dr. Valerio Baiocchi
Prof. Dr. Xianfeng Zhang
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 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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Published Papers (1 paper)

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19 pages, 3633 KiB  
Article
GGMNet: Pavement-Crack Detection Based on Global Context Awareness and Multi-Scale Fusion
by Yong Wang, Zhenglong He, Xiangqiang Zeng, Juncheng Zeng, Zongxi Cen, Luyang Qiu, Xiaowei Xu and Qunxiong Zhuo
Remote Sens. 2024, 16(10), 1797; https://doi.org/10.3390/rs16101797 - 18 May 2024
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Abstract
Accurate and comprehensive detection of pavement cracks is important for maintaining road quality and ensuring traffic safety. However, the complexity of road surfaces and the diversity of cracks make it difficult for existing methods to accomplish this challenging task. This paper proposes a [...] Read more.
Accurate and comprehensive detection of pavement cracks is important for maintaining road quality and ensuring traffic safety. However, the complexity of road surfaces and the diversity of cracks make it difficult for existing methods to accomplish this challenging task. This paper proposes a novel network named the global graph multiscale network (GGMNet) for automated pixel-level detection of pavement cracks. The GGMNet network has several innovations compared with the mainstream road crack detection network: (1) a global contextual Res-block (GC-Resblock) is proposed to guide the network to emphasize the identities of cracks while suppressing background noises; (2) a graph pyramid pooling module (GPPM) is designed to aggregate the multi-scale features and capture the long-range dependencies of cracks; (3) a multi-scale features fusion module (MFF) is established to efficiently represent and deeply fuse multi-scale features. We carried out extensive experiments on three pavement crack datasets. These were DeepCrack dataset, with complex background noises; the CrackTree260 dataset, with various crack structures; and the Aerial Track Detection dataset, with a drone’s perspective. The experimental results demonstrate that GGMNet has excellent performance, high accuracy, and strong robustness. In conclusion, this paper provides support for accurate and timely road maintenance and has important reference values and enlightening implications for further linear feature extraction research. Full article
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