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: 28 November 2025 | Viewed by 19
Special Issue Editor
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
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Keywords
- landslide detection
- real-time monitoring
- time series analysis
- deep learning
- machine learning
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