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Remote Sensing
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Landslide Detection Using Machine and Deep Learning
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Landslide Detection Using Machine and Deep Learning

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: 31 May 2026 | Viewed by 1691

Special Issue Editor

Dr. Wen Liu

E-Mail Website
Guest Editor
Graduate School of Engineering, Chiba University, Chiba 263-8522, Japan
Interests: SAR; optical images; LiDAR data; disasters; building damage; bridge damage
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to global warming, the frequency of extreme weather events is increasing, leading to a higher risk of landslides. Traditional methods for landslide detection and monitoring, while important, often face limitations in scalability, accuracy, and efficiency. Recent advances in machine learning (ML) and deep learning (DL) offer promising alternatives by enabling the analysis of large-scale, multi-source data such as remote sensing imagery, digital elevation models, and sensor networks. These approaches can enhance early warning systems, risk assessment models, and real-time monitoring, contributing to more effective disaster risk reduction and emergency response strategies.

This Special Issue aims to compile studies that explore applications of machine learning and deep learning to facilitate landslide forecasting, monitoring and detection.

This Special Issue welcomes original research articles and comprehensive reviews on the application of ML and DL techniques for landslide detection and prediction. Topics of interest include, but are not limited to, the following:

  • Landslide susceptibility mapping;
  • Time series analysis for landslide forecasting;
  • Remote sensing and image-based landslide detection;
  • The integration of multi-source data (e.g., meteorological, geological, hydrological);
  • Explainable AI and model interpretability in landslide studies;
  • Real-time landslide monitoring with sensor and IoT data.

Dr. Wen Liu
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • landslide detection
  • real-time monitoring
  • time series analysis
  • deep learning
  • machine learning

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Published Papers (2 papers)

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22 pages, 17254 KB  
Article
Landslide Susceptibility Assessment Based on a Deep Learning-Derived Landslide Inventory in Moxi Town, Sichuan, China
by Yitong Yao, Yixiang Du, Wenjun Zhang, Xianwen Liu, Jialun Cai, Hui Feng, Hongyao Xiang, Rong Hu, Yuhao Yang and Tongben Fu
Remote Sens. 2026, 18(6), 849; https://doi.org/10.3390/rs18060849 - 10 Mar 2026
Viewed by 506
Abstract
Landslides are characterized by strong suddenness and a wide range of damage; accurate prediction of their susceptibility is an important prerequisite for regional risk prevention and control. To address the difficulties in acquiring landslide inventories in complex terrain areas and the insufficient interpretability [...] Read more.
Landslides are characterized by strong suddenness and a wide range of damage; accurate prediction of their susceptibility is an important prerequisite for regional risk prevention and control. To address the difficulties in acquiring landslide inventories in complex terrain areas and the insufficient interpretability of existing prediction models, this study proposes a landslide susceptibility assessment (LSA) framework that integrates automated sample detection and interpretability analysis. The proposed framework is applied to Moxi Town, a typical alpine valley area in Sichuan Province, China. A Mask R-CNN instance segmentation model was introduced to achieve automated detection of landslide samples, resulting in a high-quality inventory containing 923 landslides. Based on the spatial relationships between the landslide inventory and influencing factors, a convolutional neural network (CNN) landslide susceptibility assessment model incorporating Shapley Additive exPlanations (SHAP) interpretability analysis was constructed. The CNN model was further compared with random forest (RF) and extreme gradient boosting (XGBoost) machine learning models. The results show that the AUC value of the CNN model has increased by 4.3% and 3.2% compared with the RF and XGBoost models, respectively, and it significantly reduces the pretzel effect of landslide susceptibility mapping (LSM). The results validate the reliability of the proposed framework, which can provide technical support for landslide disaster prevention and monitoring. Full article
(This article belongs to the Special Issue Landslide Detection Using Machine and Deep Learning)
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30 pages, 24852 KB  
Article
Multi-Source Remote Sensing Data-Driven Susceptibility Mapping of Retrogressive Thaw Slumps in the Yangtze River Source Region
by Yun Tian, Taorui Zeng, Qing Lü, Hongwei Jiang, Sihan Yang, Hang Cao and Wenbing Yu
Remote Sens. 2026, 18(3), 380; https://doi.org/10.3390/rs18030380 - 23 Jan 2026
Viewed by 524
Abstract
Despite the ecological sensitivity of the Yangtze River Source Region (YRSR), the current research critically lacks a quantified assessment of the spatial occurrence probability of Retrogressive Thaw Slumps (RTSs) in this specific high-altitude terrain. This study aims to bridge this knowledge gap by [...] Read more.
Despite the ecological sensitivity of the Yangtze River Source Region (YRSR), the current research critically lacks a quantified assessment of the spatial occurrence probability of Retrogressive Thaw Slumps (RTSs) in this specific high-altitude terrain. This study aims to bridge this knowledge gap by establishing a robust susceptibility assessment framework to accurately model the spatial distribution and risk levels of RTSs. The innovations of this research include (i) the construction of a complete and up-to-date 2024 RTS inventory for the entire YRSR based on high-resolution optical remote sensing; (ii) the integration of time-series spectral features (e.g., vegetation and moisture trends) alongside static topographic variables to enhance the physical interpretability of machine learning models; and (iii) the application of advanced ensemble learning algorithms combined with SHAP analysis to establish a comprehensive RTS susceptibility zonation. The results reveal a rapid intensification of instability, evidenced by an 83.5% surge in RTS abundance, with the CatBoost model achieving exceptional accuracy (AUC = 0.994), and identifying that specific static topographic factors (particularly elevations between 4693 and 4812 m and north-to-east aspect) and dynamic spectral anomalies (indicated by declining vegetation vigor and increasing surface wetness) are the dominant drivers controlling RTS distribution. This study provides essential baseline data and spatial guidance for ecological conservation and engineering maintenance in the Asian Water Tower, demonstrating a highly effective paradigm for monitoring permafrost hazards under climate warming. Full article
(This article belongs to the Special Issue Landslide Detection Using Machine and Deep Learning)
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