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 925
Special Issue Editors
Interests: geosystems engineering; artificial intelligence; geoinformatics; geostatistics; natural hazards; remote sensing; mineral exploration
Interests: hydrogeology; geomorphology; natural hazards; land use/cover changes; optical/radar remote sensing
Special Issues, Collections and Topics in MDPI journals
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
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
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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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