Predictive Modelling of Landslide Hotspots in Nainital using MT-InSAR and Deep Learning

Mountainous regions of the Kumaon Himalayas are particularly prone to landslides due to steep terrain, weak geological conditions, intense monsoon rainfall, and increasing human activity. In such environments, continuous ground-based monitoring is often difficult because of poor accessibility, dense vegetation cover, and frequent cloud conditions. Microwave remote sensing, especially satellite-based Synthetic Aperture Radar (SAR), offers a reliable, weather-independent means of monitoring surface deformation over large areas and long time periods.

This study applies an integrated multi-temporal InSAR (MT-InSAR) and deep learning framework to investigate surface deformation and landslide activity in the Nainital region, Kumaon Himalayas, India. Sentinel-1A SAR data (2020–2025) were processed on the ASF Vertex HyP3 cloud platform using the GAMMA Small Baseline Subset (SBAS) processing chain. The cloud-based workflow automates key interferometric steps, enabling efficient processing of multi-year SAR archives without the need for local high-performance computing facilities.

Time-series inversion and analysis were carried out using MintPy in a GPU-enabled OpenSARLab environment. Weighted least-squares inversion was applied to generate line-of-sight (LOS) deformation time-series and mean LOS velocity maps. In addition, ENU decomposition was performed, and the vertical (Up) component was used for subsequent analysis. The resulting five-year deformation record highlights marked spatial variability across the study area, reflecting deformation associated with slow-moving landslides, slope creep, and other forms of localized instability.

To focus on actively deforming areas, pixels were objectively selected using Otsu thresholding applied to long-term displacement metrics derived from the MT-InSAR time-series. This data-driven approach allowed stable and deforming areas to be separated without relying on subjective thresholds, capturing both known unstable slopes and newly emerging deformation zones. The selected high-deformation pixels were then used for short-term deformation forecasting using two models: a Long Short-Term Memory (LSTM) network and a Temporal Convolutional Network (TCN).

Model performance was assessed using five-fold time-series cross-validation. The TCN model showed consistently better performance, achieving an R² of ~0.95 and an F1-score of ~0.97, compared to the LSTM model (R² ~0.93, F1 ~0.92), indicating improved representation of long-range temporal dependencies and non-linear deformation behaviour.

To examine the spatial evolution of instability, K-means clustering was applied to both five-year historical and twelve-month forecasted displacement time-series, producing deformation cluster maps for each period. Areas showing transitions from lower to higher deformation classes were identified as emerging landslide hotspots. The observed deformation patterns and hotspot distribution show strong agreement with previous MT-InSAR-based landslide studies and regional landslide inventories from the Himalayan region, providing independent validation of the proposed framework for landslide hazard assessment and risk management.