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Remote Sensing
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Advances in Monitoring and Detection of Geohazards in Urban Areas...
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Advances in Monitoring and Detection of Geohazards in Urban Areas Using Remote Sensing

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

Deadline for manuscript submissions: closed (25 April 2024) | Viewed by 2511

Special Issue Editors

Dr. Bahareh Kalantar

E-Mail Website
Guest Editor
RIKEN Center for Advanced Intelligence Project, Goal-Oriented Technology Research Group, Disaster Resilience Science Team, Tokyo 103-0027, Japan
Interests: machine learning; remote sensing and gis; image processing; environmental modelling; object detection
Special Issues, Collections and Topics in MDPI journals
Dr. Alfian Abdul Halin

E-Mail Website
Guest Editor
Department of Multimedia, Faculty of Computer Science & Information Technology, Universiti Putra Malaysia, Serdang 43400, Malaysia
Interests: applied machine learning; computer vision; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Husam A. H. Al-Najjar

E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, Sydney 2007, Australia
Interests: spatial data analysis; natural hazards; landslides; machine/deep learning; digital earth

Special Issue Information

Dear Colleagues,

Geohazards such as landslides, earthquakes, and floods have caused significant damage to urban areas around the world, affecting both the environment and human lives. Remote sensing techniques have proven to be an effective tool for monitoring and detecting these geohazards, providing valuable information for disaster risk management and urban planning. This Special Issue aims to present recent advances in remote sensing technologies and methodologies for geohazard monitoring and detection in urban areas.

This Special Issue aims to provide a platform for researchers and practitioners to share their latest findings, insights, and experiences relating to geohazard monitoring and detection using remote sensing techniques in urban areas. This Special Issue is in line with the scope of the Remote Sensing journal, which covers the application of remote sensing technologies in various fields, including geosciences and environmental monitoring.

We invite original research articles, reviews, and case studies that address the following themes:

  • Innovative remote sensing technologies and methodologies for geohazard monitoring and detection in urban areas;
  • The integration of multiple remote sensing data sources (e.g., optical, radar, LiDAR) for geohazard mapping and analysis;
  • Machine learning and deep learning approaches for geohazard detection and classification using remote sensing data;
  • Urban resilience and disaster risk reduction strategies based on remote sensing information;
  • Case studies of geohazard monitoring and detection in urban areas using remote sensing techniques.

We welcome contributions that provide novel insights, innovative approaches, and practical implications for geohazard monitoring and detection in urban areas using remote sensing technologies.

Dr. Bahareh Kalantar
Dr. Alfian Abdul Halin
Dr. Husam A. H. Al-Najjar
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

  • geohazards
  • urban areas
  • remote sensing
  • disaster monitoring
  • risk assessment
  • environmental monitoring

Published Papers (3 papers)

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Research

23 pages, 8773 KiB  
Article
An Adaptive IMM Algorithm for a PD Radar with Improved Maneuvering Target Tracking Performance
by Wenwen Xu, Jiankang Xiao, Dalong Xu, Hao Wang and Jianyin Cao
Remote Sens. 2024, 16(6), 1051; https://doi.org/10.3390/rs16061051 - 15 Mar 2024
Viewed by 493
Abstract
A pulse-Doppler (PD) radar has the advantage of strong anti-interference ability, and it is often used as a solution for maneuvering target tracking. In the application of target monitoring and tracking in PD radars, the interacting multiple model algorithm (IMM) has become the [...] Read more.
A pulse-Doppler (PD) radar has the advantage of strong anti-interference ability, and it is often used as a solution for maneuvering target tracking. In the application of target monitoring and tracking in PD radars, the interacting multiple model algorithm (IMM) has become the main and preferred choice due to its flexibility and high accuracy. However, the probability transfer matrix in classical IMM algorithms generally depends on constant prior knowledge, and if a PD radar is tracking a strong maneuvering target, it is inevitable to encounter some limitations, such as the possibility of target tracking trajectory deviation, and even a loss of the target. The Markov probability transfer matrix is proposed with an adaptive modification ability in real time to overcome the above problems in this paper. Additionally, for improving the speed of switching between the models, the fuzzy control system for secondary updating of model probability is adopted. By this means, the tracking accuracy of maneuvering targets is enhanced. Compared with the classical IMM algorithm, the corresponding simulation results for the PD radar indicate that the overall tracking accuracy of the proposed adaptive IMM algorithm is improved by 19.6%. In conclusion, the continuity and accuracy of the target trajectory can be effectively improved with the proposed adaptive IMM algorithm in PD radar cases. Full article
(This article belongs to the Special Issue Advances in Monitoring and Detection of Geohazards in Urban Areas Using Remote Sensing)
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18 pages, 16502 KiB  
Article
Intricacies of Opening Geometry Detection in Terrestrial Laser Scanning: An Analysis Using Point Cloud Data from BLK360
by Jinman Jung, Taesik Kim, Hong Min, Seongmin Kim and Young-Hoon Jung
Remote Sens. 2024, 16(5), 759; https://doi.org/10.3390/rs16050759 - 21 Feb 2024
Viewed by 454
Abstract
This study investigates the use of terrestrial laser scanning (TLS) in urban excavation sites, focusing on enhancing ground deformation detection by precisely identifying opening geometries, such as gaps between pavement blocks. The accuracy of TLS data, affected by equipment specifications, environmental conditions, and [...] Read more.
This study investigates the use of terrestrial laser scanning (TLS) in urban excavation sites, focusing on enhancing ground deformation detection by precisely identifying opening geometries, such as gaps between pavement blocks. The accuracy of TLS data, affected by equipment specifications, environmental conditions, and scanning geometry, is closely examined, especially with regard to the detection of openings between blocks. The experimental setup, employing the BLK360 scanner, aimed to mimic real-world paving situations with varied opening widths, allowing an in-depth analysis of how factors related to scan geometry, such as incidence angles and opening orientations, influence detection capabilities. Our examination of various factors and detection levels reveals the importance of the opening width and orientation in identifying block openings. We discovered the crucial role of the opening width, where larger openings facilitate detection in 2D cross-sections. The overall density of the point cloud was more significant than localized variations. Among geometric factors, the orientation of the local object geometry was more impactful than the incidence angle. Increasing the number of laser beam points within an opening did not necessarily improve detection, but beams crossing the secondary edge were vital. Our findings highlight that larger openings and greater overall point cloud densities markedly improve detection levels, whereas the orientation of local geometry is more critical than the incidence angle. The study also discusses the limitations of using a single BLK360 scanner and the subtle effects of scanning geometry on data accuracy, providing a thorough understanding of the factors that influence TLS data accuracy and reliability in monitoring urban excavations. Full article
(This article belongs to the Special Issue Advances in Monitoring and Detection of Geohazards in Urban Areas Using Remote Sensing)
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21 pages, 6724 KiB  
Article
Large-Scale Surface Deformation Monitoring Using SBAS-InSAR and Intelligent Prediction in Typical Cities of Yangtze River Delta
by Rong Wang, Yongjiu Feng, Xiaohua Tong, Pengshuo Li, Jiafeng Wang, Panli Tang, Xiaoyan Tang, Mengrong Xi and Yi Zhou
Remote Sens. 2023, 15(20), 4942; https://doi.org/10.3390/rs15204942 - 12 Oct 2023
Cited by 1 | Viewed by 1033
Abstract
Large-scale short-term monitoring and prediction of surface deformation are of great significance for the prevention and control of geohazards in rapidly urbanizing developing cities. Most studies focus on individual cities, but it would be more meaningful to address large urban agglomerations and consider [...] Read more.
Large-scale short-term monitoring and prediction of surface deformation are of great significance for the prevention and control of geohazards in rapidly urbanizing developing cities. Most studies focus on individual cities, but it would be more meaningful to address large urban agglomerations and consider the relevance of the regions within them. In addition, the commonly used linear fitting prediction methods cannot accurately capture the dynamic mechanisms of deformation. In this study, we proposed an automatic PS extraction method (named PS-SBAS-InSAR) that improves SBAS-InSAR to extract surface deformation and an Informer-based short-term surface deformation prediction method for case studies in 16 typical cities of the Yangtze River Delta (YRD). The results show that PS-SBAS-InSAR successfully extracted accurate surface deformation sequences of the YRD. During the period from January 2019 to January 2021, the YRD experienced a slight deformation with an average deformation rate within [−4, 4] mm/year. Geographically neighboring cities may have associated deformation distributions and similar deformation trends, as indicated by average deformation rate maps and landscape metrics. Both types of deformation (i.e., subsidence/uplift) tend to occur simultaneously, with specific areas of subsidence/uplift occurring in close proximity to areas of concentrated deformation. The Informer model effectively captured the time-series variation in surface deformation, suggesting a slowdown of deformation over the next two months (February 2021–March 2021). Our work contributes to a better understanding of changes and trends in large-scale surface deformation and provides useful methods for monitoring and predicting surface deformation in coastal areas. Full article
(This article belongs to the Special Issue Advances in Monitoring and Detection of Geohazards in Urban Areas Using Remote Sensing)
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