# The Use of Earth Observation Data for Railway Infrastructure Monitoring--A Review
## 3 EO-Based Railway Infrastructure Monitoring: Literature Review
### Comparative Analysis of EO Data Use in Railway Infrastructure Monitoring
The method estimates settlements at the millimeter level and generates deformation maps. Using 213 Radarsat-2 SAR images, the approach was demonstrated over the entire railway network of the Netherlands. A method for 3D geolocation quality improvement of Sentinel-1 SAR data aided by laser scanning data, specifically Light Detection and Ranging (LiDAR) data, was tested and validated on the Betuweroute freight train track in the Netherlands [27]. It enabled the classification of railway substructures into rail tracks, embankment areas, and surrounding areas, as well as the recognition of unstable rail segments by using differential settlement of rails. The line of sight (LOS) deformation velocity map for the part of the test site is shown in Figure 1[27]. DInSAR technique for the identification of deformation of railways, which may compromise both serviceability and safety, was already used by [PERSON] et al. [41] for measuring the subsidence on railway tracks in the central sector of Ebro Valley, Spain, in particular on the Castejon-Zaragoza conventional railway line. This area is affected by evaporite karst, where soluble rocks like gypsum, anhydrite, or salt dissolve, forming sinkholes or subsurface voids. These features can destabilize railway corridors due to their subsidence and deformation potential. The TD was detected thanks to medium-resolution surface velocity maps generated through the analysis of the archived data of the ENVISAT and ALOS SAR missions. To analyze TD and GD, [PERSON] et al. [40] combined multi-temporal interferometric SAR (MT-InSAR) and LiDAR data for deformation monitoring along the railway and buffer zone near Zaltbommel in the Netherlands. LiDAR data were used to aid the attribution of radar scatters to physical objects and for the classification of railway substructures, making it easier to interpret deformation signals, especially in transition zones. Figure 2a shows the Actueel Hoogtebestand Nederland 3 (AHN3) LiDAR point cloud along the railway with classification, while Figure 2e shows the location of the railway. Figure 2b-d show estimated parameters on both continuously coherent scatterers (CCS) and temporary coherent scatterers (TCS). Another type of InSAR technology, persistent scatterer interferometric SAR (PS-InSAR), was used by [PERSON] et al. [43] to measure and analyze rail TD and examine deformation trends in a part of the South Korean railway network. Reflection intensity was increased by installing corner reflectors in topologically different areas of the railway. The primary focus was on testing and verification where the on-site measurements were carried out, and SAR images were captured for the same period. To assess GD, [PERSON] et al. [20] combined SAR data and data from local GNSS reference stations for monitoring railway lines passing through Provadia town in Bulgaria close to the salt deposit, an area known for ground sinking caused by intensive mining activity. GNSS data were used to validate the SAR-based pilot system. The DInSAR (differential synthetic aperture radar) approach was used to process the SAR data to measure ground motion. Based on monitoring approximately 50 km of railway tracks in Northern Switzerland, [PERSON] et al. [21] proved that there exists a correlation between persistent scatterer interferometry (PSI) GD variability and chord-based measurements of longitudinal height performed by a survey train. Chord-based measurements, as suggestive indicators for detecting rail track deformations interpreted by experts, are limited to specific rail track sections in which PSI-based measurements have no limitations. Railway track classification was performed by setting thresholds on the PSI statistical features. The PSI-based approach was tested using high-resolution TerraSAR-X observations. Land subsidence along the Beijing-Tianjin high-speed railway in China was investigated using ground deformation results derived from two SAR platforms, ENVI-SAT/ASAR and TerraSAR-X [38]. The data were fused using the minimum gradient difference of a fitting curve.