## 6 Conclusions and Future Work  
This study presents InSAR-RiskLSTM, a comprehensive framework designed to predict railway deformation risks by integrating Interferometric Synthetic Aperture Radar (InSAR) data with temporal Long Short-Term Memory (LSTM) networks and spatial attention mechanisms. The framework addresses the inherent challenges of spatiotemporal dependencies in railway deformation scenarios, offering significant advancements in combining image-based monitoring with predictive analytics. By leveraging the complementary strengths of LSTM and attention mechanisms, the proposed approach enhances data fusion capabilities, leading to improved accuracy and reliability in risk assessment and early warning systems for railway infrastructure.  
The model is particularly beneficial for stakeholders involved in railway infrastructure management and maintenance. Railway operators can use InSAR-RiskLSTM to optimize maintenance schedules and allocate resources more efficiently, reducing both costs and downtime. Government agencies and transportation authorities can leverage its predictive capabilities to enhance railway safety regulations and implement early warning systems for infrastructure failures. Engineering and geospatial analytics firms can integrate the model into existing risk assessment platforms, providing advanced monitoring solutions for large-scale transportation networks. Additionally, insurance companies and investment firms involved in railway projects can use the model to quantify infrastructure risks and make informed financial decisions. Beyond railway systems, the framework's adaptability enables broader applications in transport infrastructure monitoring, including highways, bridges, and pipelines. By leveraging high-resolution InSAR data and deep learning techniques, InSAR-RiskLSTM contributes to the development of smarter, more resilient transportation networks, supporting long-term sustainability and safety in global transport systems.  
While the results underscore the potential of InSAR-RiskLSTM in advancing image-based methodologies within cyber-physical systems, the framework is not without limitations. The reliance on high-quality InSAR data can pose challenges in regions with sparse or noisy data. Future work could explore advanced noise-cancellation techniques and methods to improve data quality, ensuring robust performance across diverse environments. Additionally, the computational demands of the spatial attention mechanism may limit the framework's applicability in real-time scenarios. Optimizing or developing lightweight attention models could address these challenges, enabling broader scalability and deployment. Expanding the framework to incorporate multi-source data, such as real-time weather information, structural health monitoring (SHM) data, and additional geophysical inputs, represents another promising direction for future research. Such integrations would facilitate a more comprehensive and resilient prediction system, further enhancing the utility of InSAR-RiskLSTM in safeguarding critical railway infrastructure. These enhancements align with the ongoing advancements in cyber-physical systems, highlighting the framework's potential to drive innovation in predictive modeling and infrastructure management.  
Conceptualization, B.L.; methodology, B.L.; software, B.L.; validation, B.L.; formal analysis, B.L.; investigation, H.D.F.; data curation, B.L.; writing--original draft preparation, Z.Z.; writing--review and editing, Z.Z.; visualization, Z.Z.; supervision, H.D.F.; funding acquisition, H.D.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The dataset and code are available at [[https://github.com/LyuBaihang2024/InSAR-RiskLSTM.git](https://github.com/LyuBaihang2024/InSAR-RiskLSTM.git)]([https://github.com/LyuBaihang2024/InSAR-RiskLSTM.git](https://github.com/LyuBaihang2024/InSAR-RiskLSTM.git)), accessed on 20 December 2024.  
The authors declare no conflicts of interest.