InSAR-RiskLSTM: Enhancing Railway Deformation Risk Prediction with Image-Based Spatial Attention and Temporal LSTM Models

Railway infrastructure faces significant operational threats due to ground deformation risks from natural and anthropogenic sources, posing serious challenges to safety and maintenance. Traditional monitoring methods often fail to capture the complex spatiotemporal patterns of railway deformation, leading to delayed responses and increased risks of infrastructure failure. To address these limitations, this study introduces InSAR-RiskLSTM, a novel framework that leverages the high-resolution and wide-coverage capabilities of Interferometric Synthetic Aperture Radar (InSAR) to enhance railway deformation risk prediction. The primary objective of this study is to develop an advanced predictive model that accurately captures both temporal dependencies and spatial susceptibilities in railway deformation processes. The proposed InSAR-RiskLSTM framework integrates Long Short-Term Memory (LSTM) networks with spatial attention mechanisms to dynamically prioritize high-risk regions and improve predictive accuracy. By combining image-based spatial attention for deformation hotspot identification with advanced temporal modeling, the approach ensures more reliable and proactive risk assessment. Extensive experiments on real-world railway datasets demonstrate that InSAR-RiskLSTM achieves superior predictive performance compared to baseline models, underscoring its robustness and practical applicability. The results highlight its potential to contribute to proactive railway maintenance and risk mitigation strategies by providing early warnings for infrastructure vulnerabilities. This work advances the integration of image-based methods within cyber–physical systems, offering practical tools for safeguarding critical railway networks.