Multi-Sensor Remote Sensing and Advanced Computational Frameworks for Landslide Detection and Predictive Modeling

Dear Colleagues,

Landslides rank among the most devastating natural hazards, leading to considerable human casualties and economic disruptions globally. These events are precipitated by a complex interplay of climatic, geological, geomorphological, and anthropogenic drivers, making them inherently difficult to monitor and predict using conventional methodologies. Their sudden onset, spatial variability, and localized effects underscore the need for sophisticated tools capable of delivering precise detection, mapping, and forecasting.

This Special Issue aims to advance the discourse on landslide research by exploring the integration of multi-sensor remote sensing (RS) technologies with state-of-the-art computational frameworks. Innovations in sensor platforms—spanning optical, radar (including InSAR and LiDAR), thermal, and hyperspectral modalities—are resulting in the generation of high-resolution, multidimensional datasets. These resources are critical for observing slope dynamics, assessing soil moisture variations, monitoring vegetation health, and detecting early signs of terrain instability.

Parallel to these technological advances is the evolution of machine learning (ML) and artificial intelligence (AI) methods, which are increasingly employed to analyze vast and heterogeneous datasets. From conventional approaches such as decision trees and support vector machines to advanced deep learning architectures (e.g., CNNs, RNNs, autoencoders), ensemble learning strategies (e.g., bagging, boosting, stacking), and nature-inspired algorithms (e.g., genetic algorithms, particle swarm optimization), ML tools are instrumental in enhancing landslide susceptibility mapping, risk evaluation, and predictive modeling across spatial and temporal scales.

Geographic information systems (GISs) further enrich these analytical efforts by providing geospatial context, enabling integrated data analysis, and supporting terrain modeling and scenario simulations. Furthermore, the advent of explainable AI (XAI) addresses a key limitation of traditional black-box models by enhancing the interpretability of ML outputs. XAI techniques promote transparency, facilitating a better understanding of geofactor influences and fostering greater trust in model predictions—an essential aspect for the deployment of landslide early warning systems and decision-making frameworks.

We invite submissions that demonstrate the convergence of multi-sensor RS data, cutting-edge ML techniques, GIS applications, and XAI frameworks to address critical challenges in landslide science. Emphasis should be placed on methodological innovation, real-world applicability, and interdisciplinary collaboration pushing the boundaries of landslide detection, monitoring, mapping, and forecasting.

Potential topics of interest include, but are not limited to, the following:

• Regional or global case studies on landslide risk phenomena prediction and assessment.
• Integration of multi-source remote sensing data for early landslide detection and real-time monitoring.
• Software development and implementation of machine learning, optimization, deep learning techniques, and meta-heuristic algorithms.
• Application of explainable AI (XAI) in interpreting model outputs for landslide prediction.
• Cloud-based platforms and big data processing for large-scale landslide risk analysis.

This Special Issue, entitled “Multi-Sensor Remote Sensing and Advanced Computational Frameworks for Landslide Detection and Predictive Modeling”, seeks to advance this field by integrating innovative techniques and cultivating a deeper understanding of landslide phenomena through explainable AI and other cutting-edge technologies.

We look forward to your contributions.

Dr. Paraskevas Tsangaratos
Dr. Wei Chen
Dr. Ioanna Ilia
Guest Editors

Manuscript Submission Information

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• earth observation data—remote sensing technology
• geographic information systems
• machine learning, soft computing
• landslide susceptibility, hazardous, and risk mapping
• explainable AI in landslide phenomena prediction