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Global, Regional and Cross-Event Transferability of Deep Learning and Machine Learning Models for Landslide Detection and Susceptibility Mapping

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 1851

Special Issue Editors

Dr. Adel Asadi

E-Mail Website
Guest Editor
Department of Earth and Planetary Sciences, Stanford University, Stanford, CA 94305, USA
Interests: geosystems engineering; artificial intelligence; geoinformatics; geostatistics; natural hazards; remote sensing; mineral exploration
Dr. Magaly Koch

E-Mail Website
Guest Editor
Center for Remote Sensing, Boston University, Boston, MA 02215, USA
Interests: hydrogeology; geomorphology; natural hazards; land use/cover changes; optical/radar remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Weiwei Zhan

E-Mail Website
Guest Editor
Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA
Interests: geohazard assessment and mitigation; geotechnical earthquake engineering; remote sensing and GIS; AI for natural hazard engineering; uncertainty quantification; computational geomechanics; multi-hazard infrastructure resilient design
Dr. Xiaokang Zhang

E-Mail Website
Guest Editor
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon TU428, Hong Kong
Interests: remote sensing; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

This Special Issue is dedicated to exploring the transferability of deep learning (DL) and machine learning (ML) models in landslide detection and susceptibility mapping. As climate change intensifies and urbanization increases, the frequency and severity of landslides pose a significant threat to communities and infrastructure. Developing robust models that can be effectively applied across different geographic regions and varying event conditions is critical to enhance disaster preparedness and mitigation efforts.

DL and ML models have demonstrated high accuracy in identifying landslide-prone areas within specific study regions. However, their performance often diminishes when applied to new regions or different types of landslide events due to variations in topography, landcover, soil composition, climatic conditions, data availability, etc. This Special Issue aims to address these challenges by showcasing research that enhances the global, regional and cross-event transferability of these models.

To promote the development of universally applicable models that can significantly enhance landslide risk assessment and management, we seek contributions that cover a wide range of topics, including, but not limited to, the following:

  • Development and application of innovative algorithms for landslide mapping model generalization;
  • Integration of multi-source and multi-temporal remote sensing data for landslide monitoring;
  • Comparative analyses of model performance across diverse terrains and climatic conditions;
  • Interdisciplinary approaches combining geospatial analysis, hydrology and earth sciences;
  • Development of global, continental, regional or country-scale geospatial landslide susceptibility;
  • Presenting multi-regional landslide inventories, imagery and geospatial data as ground-truth.

Dr. Adel Asadi
Dr. Magaly Koch
Dr. Weiwei Zhan
Dr. Xiaokang 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

  • landslide detection
  • landslide susceptibility mapping
  • earthquakes and rainfalls
  • change detection
  • global and regional models
  • multi-source remote sensing
  • image processing
  • geospatial modeling
  • deep transfer learning
  • machine learning

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Published Papers (2 papers)

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Review

44 pages, 10575 KiB  
Review
Application of Artificial Intelligence in Landslide Susceptibility Assessment: Review of Recent Progress
by Muratbek Kudaibergenov, Serik Nurakynov, Berik Iskakov, Gulnara Iskaliyeva, Yelaman Maksum, Elmira Orynbassarova, Bakytzhan Akhmetov and Nurmakhambet Sydyk
Remote Sens. 2025, 17(1), 34; https://doi.org/10.3390/rs17010034 - 26 Dec 2024
Viewed by 655
Abstract
In the current work, authors reviewed the latest research results in landslide susceptibility mapping (LSM) using artificial intelligence (AI) methods. Based on an overall review of collected publications, the review was classified into four sections based on their complexity: single-model approaches, enhanced models [...] Read more.
In the current work, authors reviewed the latest research results in landslide susceptibility mapping (LSM) using artificial intelligence (AI) methods. Based on an overall review of collected publications, the review was classified into four sections based on their complexity: single-model approaches, enhanced models with optimization, ensemble models, and hybrid models. Each category offers distinct advantages and is suited to specific geographic and data conditions, enabling the selection of an optimal model type based on the complexity and requirements of the mapping task. Among models, random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and multilayer perception (MLP) are used as the baseline to compare any new model introduced to develop LSM. Moreover, compared to previous review works, the number of LSM conditioning factors used in AI models are significantly increased, up to 122 factors. Their relation to the AI models is illustrated using Sankey diagram, while a radar chart is used to further visualize the dataset size per reviewed work for comparative purposes. In the main part of the current review work, the main findings are summarized into a table form, where the reader can find the overall relations between landslide conditioning factors, landslide dataset size, applied AI models, and their accuracy on predicting LSM for selected geographical locations. In terms of the regions, Asia is leading in the application of AI models to generate LSM, and in such regions with dense populations falling into higher landslide risk categories, there are more ongoing research activities, using modern AI methods. This trend underscores the increased use of AI in disaster management, with implications for improving practical applications, such as early warning systems and informing policy decisions aimed at risk reduction in vulnerable areas. Full article
(This article belongs to the Special Issue Global, Regional and Cross-Event Transferability of Deep Learning and Machine Learning Models for Landslide Detection and Susceptibility Mapping)
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33 pages, 8038 KiB  
Review
Insights Gained from the Review of Landslide Susceptibility Assessment Studies in Italy
by Samuele Segoni, Rajendran Shobha Ajin, Nicola Nocentini and Riccardo Fanti
Remote Sens. 2024, 16(23), 4491; https://doi.org/10.3390/rs16234491 - 29 Nov 2024
Viewed by 708
Abstract
We conducted a systematic literature review of 105 landslide susceptibility studies in Italy from 1980 to 2023, retrieved from the Scopus database. We discovered that Italian researchers primarily focus on rainfall-induced landslides (86.67% of the articles), especially shallow and fast movements (60%), with [...] Read more.
We conducted a systematic literature review of 105 landslide susceptibility studies in Italy from 1980 to 2023, retrieved from the Scopus database. We discovered that Italian researchers primarily focus on rainfall-induced landslides (86.67% of the articles), especially shallow and fast movements (60%), with 72% of studies conducted at the local scale, while regional and national-level studies are rare. The most common data sources include remote sensing images validated by field surveys and official data portals at the national or regional level. Data splitting usually follows a 70:30 ratio and 24 modelling techniques were identified, with logistic regression being historically prevalent, although machine learning methods have rapidly gained popularity. Italian studies used 97 predisposing factors, with slope angle (98.09%), lithology (89.52%), land use/land cover (78.09%), and aspect (77.14%) being the most employed. This review also identifies and discusses a few less-used factors, like soil sealing, rainfall, NDVI, and proximity to faults, which showed promising results in experimental studies. Predisposing factors are generally selected by expert judgment, but methods for forward factors selection and collinearity tests are becoming more common. This review synthesizes current knowledge, pinpointing gaps, highlighting emerging methodologies, and suggesting future research directions for better integration of susceptibility studies with landslide risk management. Full article
(This article belongs to the Special Issue Global, Regional and Cross-Event Transferability of Deep Learning and Machine Learning Models for Landslide Detection and Susceptibility Mapping)
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