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Artificial Intelligence for Slope Stability and Related Infrastructure

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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

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Keywords

remote sensing
slope instability
natural hazards
infrastructure
disaster management

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Research

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24 pages, 62275 KiB

Open AccessArticle

Examining the Spatially Varying Relationships between Landslide Susceptibility and Conditioning Factors Using a Geographical Random Forest Approach: A Case Study in Liangshan, China

by Xiaoliang Dai, Yunqiang Zhu, Kai Sun, Qiang Zou, Shen Zhao, Weirong Li, Lei Hu and Shu Wang

Remote Sens. 2023, 15(6), 1513; https://doi.org/10.3390/rs15061513 - 09 Mar 2023

Cited by 8 | Viewed by 2172

Abstract

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

(This article belongs to the Special Issue Artificial Intelligence for Slope Stability and Related Infrastructure)

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47 pages, 4922 KiB

Open AccessReview

Deep Learning for Exploring Landslides with Remote Sensing and Geo-Environmental Data: Frameworks, Progress, Challenges, and Opportunities

by Qi Zhang and Teng Wang

Remote Sens. 2024, 16(8), 1344; https://doi.org/10.3390/rs16081344 - 11 Apr 2024

Viewed by 495

Abstract

This article offers a comprehensive AI-centric review of deep learning in exploring landslides with remote-sensing techniques, breaking new ground beyond traditional methodologies. We categorize deep learning tasks into five key frameworks—classification, detection, segmentation, sequence, and the hybrid framework—and analyze their specific applications in landslide-related tasks. Following the presented frameworks, we review state-or-art studies and provide clear insights into the powerful capability of deep learning models for landslide detection, mapping, susceptibility mapping, and displacement prediction. We then discuss current challenges and future research directions, emphasizing areas like model generalizability and advanced network architectures. Aimed at serving both newcomers and experts on remote sensing and engineering geology, this review highlights the potential of deep learning in advancing landslide risk management and preservation. Full article

(This article belongs to the Special Issue Artificial Intelligence for Slope Stability and Related Infrastructure)

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