Machine Learning-Enhanced InSAR for Landslide Mapping: Differentiating Landslides from Ordinary Ground Movements

Landslide inventory mapping over large, remote, and inaccessible areas remains a significant challenge due to the limitations of traditional field-based methods. Satellite-based InSAR (Interferometric Synthetic Aperture Radar) technology offers a viable solution by enabling the detection of surface displacements with millimeter-level precision, providing spatially extensive coverage of potentially landslide-prone areas. However, the accuracy of landslide detection using InSAR data can be compromised by the difficulty of distinguishing landslides from other types of surface deformation, such as subsidence, natural settlement, or deforestation, which can mimic landslide behavior in InSAR data. To address these challenges, we propose a machine learning-based approach that integrates InSAR-derived displacement time-series data with advanced pattern recognition techniques to identify and classify landslides, distinguishing them from ordinary ground movements. The methodology combines the high spatial and temporal resolution of InSAR with machine learning algorithms to recognize the distinctive features of landslides, such as sudden, non-linear displacements, velocity patterns, and deformation history. Feature engineering plays a crucial role, as key features like displacement rate, time-series patterns, and spatial characteristics (e.g., slope and curvature) are extracted from InSAR data to train machine learning models. These models can learn to differentiate between landslides and other ground movements by recognizing underlying patterns specific to landslide behavior. Supervised learning techniques are employed using labeled data (known landslide locations) to train models that can classify landslides accurately, even in areas with limited prior field data. In cases where labeled data is sparse, unsupervised learning techniques, such as clustering and anomaly detection, are applied to identify unusual displacement patterns that might indicate landslides. These models provide valuable insights into regions where landslides may occur, helping to distinguish between true landslide events and other non-landslide related surface changes. By integrating InSAR with machine learning-driven landslide pattern recognition, this approach enhances the accuracy and efficiency of landslide inventory mapping, particularly in large and remote areas where traditional field assessments are impractical. This methodology offers a scalable solution for early landslide detection, risk assessment, and hazard mapping. We discuss the potential benefits, challenges, and future directions of this approach, highlighting its applicability in diverse geographical settings and its role in advancing landslide monitoring and management strategies.