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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: 15 January 2025 | Viewed by 68

Special Issue Editors

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

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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

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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
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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 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.


  • 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

Published Papers

This special issue is now open for submission.
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