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Special Issue "Artificial Intelligence for Slope Stability and Related Infrastructure"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: 20 February 2024 | Viewed by 1421

Special Issue Editors

School of Engineering and Built Environment, Griffith University, Parklands Drive, Southport, QLD 4222, Australia
Interests: rock slope stability; remote sensing; rock mechanics; landslides
Special Issues, Collections and Topics in MDPI journals
CEO of Aiclops Inc., 283, Goyangdae-Ro, Ilsanseo-Gu, Goyang-Si 10223, Gyeonggi-Do, Republic of Korea
Interests: image analysis; photogrammetry; IoT; safety monitoring; geotechnical engineering
Special Issues, Collections and Topics in MDPI journals
Department of Software and Computer Engineering, Korea Aerospace University, Goyang-si 10540, Gyeonggi-do, Republic of Korea
Interests: data science; computer vision; artificial intelligence

Special Issue Information

Dear Colleagues,

The application of remote sensing in landslide detection, prevention, and monitoring has drawn massive attention from researchers and practitioners worldwide. Various remote sensing methods have been utilized to detect landslide movements, conduct landslide hazard assessments, investigate changes in groundwater conditions, and monitor various surface processes caused by this natural phenomenon. It has also been vitally important to monitor the stability of the adjoining engineering infrastructure, which has been put in place to cope with landslide-related issues.

This clearly generates a substantial amount of data, which often become ‘too big’ to deal with when using traditional remote sensing techniques. Fortunately, the latest developments in artificial intelligence (AI), including machine learning (ML), deep learning (DL), and artificial neural networks, have provided an alternative solution that can assist researchers, engineers, and decision-makers with developing safer and more resilient infrastructure.

This issue welcomes all publications primarily focused on the use of artificial intelligence in relation to landslide detection and monitoring, landslide hazards, and landslide-related problems that can affect the stability of infrastructure.

Topics of interest include, but are not limited to:

  • Application of remote sensing in various slope movements;
  • Damage identification and judgment of buildings and infrastructure;
  • Disaster management.

Remote sensing technologies relevant to this Special Issue include satellite and ground-based InSAR, photogrammetry, airborne- and drone-based sensors, laser scanning, etc.

Dr. Ivan Gratchev
Dr. Dong-Hyun Kim
Prof. Dr. Sung-Yup Ohn
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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.


  • remote sensing
  • slope instability
  • natural hazards
  • infrastructure
  • disaster management

Published Papers (1 paper)

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Examining the Spatially Varying Relationships between Landslide Susceptibility and Conditioning Factors Using a Geographical Random Forest Approach: A Case Study in Liangshan, China
Remote Sens. 2023, 15(6), 1513; - 09 Mar 2023
Cited by 4 | Viewed by 1046
Landslide susceptibility assessment is an important means of helping to reduce and manage landslide risk. The existing studies, however, fail to examine the spatially varying relationships between landslide susceptibility and its explanatory factors. This paper investigates the spatial variation in such relationships in [...] Read more.
Landslide susceptibility assessment is an important means of helping to reduce and manage landslide risk. The existing studies, however, fail to examine the spatially varying relationships between landslide susceptibility and its explanatory factors. This paper investigates the spatial variation in such relationships in Liangshan, China, leveraging a spatially explicit model, namely, geographical random forest (GRF). By comparing with random forest (RF), we found that GRF achieves a higher performance with an AUC of 0.86 due to its consideration of the spatial heterogeneity among variables. GRF also provides a higher-quality landslide susceptibility map than RF by correctly placing 92.35% of the landslide points in high-susceptibility areas. The local feature importance derived from GRF allows us to understand that the impact of conditioning factors varies across space, which can provide implications for policy development by local governments to place different levels of attention on different conditioning factors in specific counties to prevent and mitigate landslides. To account for the spatial dependence among the data in the model performance assessment, we use spatial cross-validation (CV) to split the data into subsets spatially rather than randomly for model training and testing. The results show that spatial CV can effectively address the over-optimistic bias in model error evaluation. Full article
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