# 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 maximum entropy model was used to correlate measured ground subsidence with three hydrogeological factors, which were identified as the likely causes of subsidence. Huge-scale railway infrastructure damage caused by an earthquake near Ojiya city, Nagaoka city, Kawaguchi town, Horinouchi town, and Yamakoshi village in Japan was detected based on commercial high-resolution SPOT optical satellite images using Normalized Difference Vegetation Index (NDVI) and statistical texture analysis [42]. The primary assumption was that changes in the vegetation cover could be used to extract damage caused by earthquakes. Statistical texture analysis was used for areas with no vegetation. Detection of damage enabled the identification of the emergency transportation route. [PERSON] et al. [44] analyzed the possible application of the PS-InSAR technique for measurements during underground work on the Cassia-Monte Mario tunnel in Rome and the High-Speed/High-Capacity Bologna Node tunnel and the Scianina-Tracoccia tunnel in Italy. The technique was also applied in the conceptual design of the new Venice-Trieste railway line. The PS-InSAR was developed and patented by the technical university Politecnico di Milano. It enables the measurement of the position of the ground target, the annual average displacement rate, and the displacement time series. The analyzed satellite data used were data gathered by the ESA ERS-1 and ERS-2 sensors and by the CSA RADARSAT-1 satellite. Ground subsidence in the proximity of several railways in the Lombardia region, near Milano city and between Lecco and Como cities in Northern Italy, was detected using two datasets of X-band Cosmo-SkyMed InSAR data [45]. Subsidence was induced by industrial development or hydrogeological factors. Interesting approach for detecting changes in railway curves caused by seismic events was proposed by [PERSON] et al. [52], using high-resolution IKONOS stereo satellite images. An empirical experiment conducted in change detection in railway curves resulting from Wenchuan earthquake in 2008 in Dujiangyan City (China) demonstrated the effectiveness of this approach. The study compared five change detection models, with the affine model achieving the highest accuracy of 0.318 m. The experimental results show that the proposed approach can detect geometric changes in railway curves caused by earthquakes with decimeter-level precision. [PERSON] et al. [57] applied differential SAR interferometry (DInSAR) using Sentinel-1 imagery for the monitoring of land subsidence along railway segment of Barcelona (Spain). The study focused on the analysis of the role of quality indicators in the assessment of DInSAR products. The experimental results obtained pointed that the quality indicators can be used for identifying reliable measurements. Using Sentinel-1 imagery and the New Small Baseline Subset (NSBAS) algorithm, [PERSON] et al. [58] assessed ground subsidence in the Kashan plain and its impact on the railway in up-down and east-west directions. It was observed that more than 60% of the railway is affected by subsidence, while the rate of vertical railway deformation was found to be between 0 and \(-\)23 mm/year. In addition, the authors proposed a method for railway stability analysis based on longitudinal profiles parallel to the rails. Permanfrost-related deformations pose significant challenges to the stability and safety of railway infrastructure in cold regions, requiring advanced GD monitoring techniques. Deformations of the permafrost subgrades (sliced and crushed rock embankments and railway bridge) at the Beiluhe test site of the Qinghai-Tibet railway in China were measured using the differential interferometric SAR (D-InSAR) technique [46]. A settlement was identified as the main subgrade deformation. Railway bridges experienced the most minor deformation among the subgrades. The same track was also considered by [PERSON] and [PERSON] [49], who investigated the use of satellite SAR imagery to detect permafrost-related instabilities, as did [PERSON] et al. [46].