## 2 Related Work  
### InSAR for Infrastructure Deformation Monitoring  
Interferometric Synthetic Aperture Radar (InSAR) has become a widely utilized remote sensing technology in monitoring infrastructure deformation, including railways, bridges, and buildings. InSAR techniques leverage phase differences between radar images acquired at different times to measure ground and infrastructure movement with millimeter-level accuracy [17]. This capability is particularly valuable for continuous monitoring and early detection of potential deformations in critical infrastructure, aiding in the mitigation of safety risks associated with ground subsidence, landslides, and structural strain [18]. The traditional InSAR methods include Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS), both of which have been extensively validated for identifying gradual and abrupt deformations across diverse environments. The use of InSAR in railway deformation monitoring has garnered significant attention due to its non-invasive nature and ability to cover extensive geographic areas [19]. Railway infrastructures are often subject to continuous strain from natural and anthropogenic factors, including seasonal ground movement, underground mining, and construction activities. InSAR data, when applied to railways, can facilitate the monitoring of long-term deformation patterns, enabling the proactive maintenance of rail networks. Recent advancements in InSAR data processing have enabled the extraction of more precise deformation signals, further enhancing the accuracy of deformation risk assessment. The challenges in using InSAR for railway deformation risk prediction include the complex interaction of various deformation sources and the need to integrate InSAR data with other contextual information to improve predictive accuracy [20]. Research is increasingly focusing on multi-source data fusion, combining InSAR data with other geospatial and sensor data to provide a more comprehensive understanding of deformation phenomena. Integration with machine learning models, particularly deep learning architectures, has shown promise in capturing complex spatiotemporal dependencies, enabling more accurate and reliable predictions for infrastructure at risk of deformation [21]. Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing technique that measures ground deformation with high precision by analyzing the phase differences between radar images captured at different times [22]. The phase of a radar signal carries information about the distance between the radar sensor and the ground surface. By comparing the phase of two or more radar images, InSAR can detect minute changes in surface elevation, often with sub-millimeter accuracy. This capability makes it an essential tool for monitoring ground movements over large areas, even in regions that are difficult to access. InSAR techniques rely on the generation of interferograms, which are created by calculating the phase differences between corresponding pixels in two radar images [23]. To derive accurate deformation measurements, several processing steps are required, including phase unwrapping, which resolves ambiguities in phase values, and geocoding, which converts radar coordinates into geographic coordinates [24]. Advanced methods such as Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) further enhance InSAR's capability by focusing on stable radar reflectors or minimizing the impact of temporal and geometric decorrelation. These methods enable the detection of both gradual and abrupt deformations across a variety of terrains and infrastructures [25]. Despite its advantages, InSAR faces challenges such as noise introduced by atmospheric effects and limitations in data availability for certain regions [26]. However, its non-invasive nature and ability to provide continuous spatial coverage have made it an indispensable tool for infrastructure monitoring, including railway deformation analysis.