## 1 Introduction  
Railway infrastructure is vulnerable to deformation risks caused by both natural and anthropogenic factors, including soil displacement, hydrological changes, and seismic activity [1]. These deformations pose serious threats to railway safety and operational reliability, making timely risk assessment and predictive maintenance critical for mitigating potential failures [2]. Traditional railway monitoring techniques, such as ground-based sensors and manual inspections, often fail to capture the complex spatiotemporal dynamics of deformation processes due to their limited spatial coverage and temporal resolution [3]. As a result, these conventional approaches may lead to delayed responses and increased infrastructure vulnerability. The initial efforts in addressing railway deformation risks relied heavily on symbolic AI and rule-based systems, which depended on domain expertise to define static thresholds and heuristic rules [4]. While these approaches offered structured interpretability, their limitations in generalizing across varied environments and adapting to time-varying conditions became evident [5]. The inability to process large-scale dynamic data further underscored the need for more adaptable frameworks [6]. The advent of data-driven models marked a significant evolution in this domain. Machine learning techniques such as decision trees, support vector machines, and random forests harnessed historical InSAR data to uncover the underlying deformation patterns [7; 8]. However, these methods fell short in capturing temporal continuity and spatial correlations, revealing a gap that necessitated the development of advanced architectures [9]. Deep learning methodologies, particularly recurrent neural networks (RNNs), Long Short-Term Memory (LSTM) networks, and attention mechanisms, have emerged as powerful tools for addressing these limitations [10]. LSTM networks excel in modeling long-term temporal dependencies that are crucial for identifying deformation trends [11], while attention mechanisms enhance spatial prioritization by dynamically focusing on high-risk areas [12]. Despite their advantages, these deep learning models introduce challenges related to interpretability and computational efficiency, particularly when dealing with large-scale spatiotemporal datasets [13]. Effective solutions require the integration of spatial and temporal components within optimized frameworks to ensure both predictive accuracy and computational feasibility [14]. To address these challenges, remote sensing technologies, particularly Interferometric Synthetic Aperture Radar (InSAR), have emerged as powerful tools for large-scale deformation monitoring [15]. InSAR provides long-term high-resolution observations of ground displacement, enabling the detection of subtle deformations that may precede infrastructure failures. However, raw InSAR data alone do not directly translate into actionable risk assessments [16]. The complex spatial dependencies and temporal trends in railway deformation demand advanced analytical methods that can effectively integrate both dimensions. The primary objective of this study is to develop an effective predictive model that leverages both the spatial and temporal aspects of InSAR data to improve railway deformation risk assessment. To achieve this, we propose InSAR-RiskLSTM, a novel framework that integrates Long Short-Term Memory (LSTM) networks with spatial attention mechanisms. This approach enables the model to dynamically prioritize high-risk regions while capturing long-term temporal dependencies in deformation processes. By combining image-based spatial attention with sequential modeling, our method enhances predictive accuracy and robustness, facilitating proactive railway maintenance and risk mitigation. Extensive experiments conducted on real-world railway datasets demonstrate that InSAR-RiskLSTM significantly outperforms the baseline models in predictive performance, reinforcing its practical applicability for infrastructure monitoring.