Review article: Deep Learning for Potential Landslide Identification: Data, Models, Applications, Challenges, and Opportunities
Abstract. As global climate change and human activities escalate, the frequency and severity of landslide hazards have been increasing. Early identification, as an important prerequisite for monitoring, evaluation, and prevention, has become increasingly critical. Deep learning, as a powerful tool for data processing and analysis, has shown significant potential in advancing landslide identification, particularly in the automated processing and analysis of remote sensing, geological, and terrain data. This review provides an overview of recent advancements in the utilization of deep learning for potential landslide identification. First, the sources and characteristics of landslide data are summarized, including satellite observation data, airborne remote sensing data, and ground-based observation data. Next, several commonly used deep learning models are classified based on their roles in potential landslide identification, such as image analysis and processing, time series analysis. Then, the role of deep learning in identifying rainfall-induced landslides, earthquake-induced landslides, human activity-induced landslides, and multi factor-induced landslides is summarized. Although deep learning has shown certain successes in landslide identification, it still faces several challenges, such as data imbalance, insufficient generalization capabilities of the models, and the complexity of landslide mechanism research. Finally, the future directions in the field are discussed. It is suggested that by combining knowledge-driven and data-driven methods for potential landslide identification, deep learning holds broad prospects for future applications in this field.