# 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
It addresses the challenge of poor image quality by incorporating preprocessing, edge detection, and morphological analysis. The algorithm can identify damaged tracks, crossings, and obstacles, helping to prevent accidents. [PERSON] et al. [60] presented a review study on the feasibility of using satellite imaging for risk management in railway turnout component failures. It examines how satellite imagery can help monitor turnout sub-systems and improve risk mitigation. The study highlights that advancements in satellite sensor quality could eventually enable the detection of more subtle deformations, such as track irregularities and buckling, enhancing railway inspection strategies and risk management. [PERSON] et al. [22] used InSAR techniques to monitor the stability of the part of the Bettworetute (double-track freight railway) close to Rotterdam. They developed algorithms to fine-tune InSAR technology to railway infrastructure, enabling near-real-time monitoring of changes in rail track geometry, with a focus on dynamic behavior and the transition zones between free embankments and fixed substructures. The developed algorithm was evaluated and validated on a segment of the Bettworetute in the Netherlands, using 248 TerraSAR-X satellite images acquired bi-weekly in a time interval from 2009 until 2013. The achieved results showed that track segments on the monitored route were subject to significant vertical displacements of up to 5 cm, most likely due to settlement or compaction. Further research by the same authors resulted in the first nationwide, satellite-based railway monitoring system with temporal updates and detection of risk areas [26]. [PERSON] et al., for this purpose, developed a probabilistic method for InSAR time series postprocessing to efficiently analyze the data, with the goal of estimating the most prob \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Paper**} & \multirow{2}{*}{**Application**} & \multicolumn{2}{c}{**Used**} & \multicolumn{2}{c}{**Evaluation/Validation Tests**} \\  & & **Satellite Data** & **Satellite** & **Satellite** & **Evaluation (E)/Testing (T)/** & **Comments** \\  & & & **SAR/Optical (O)** & **Validation (V)** & **Comments** \\ \hline \multirow{3}{*}{[48]} & VG & \multicolumn{4}{c}{Sentinel-1, Sentinel-2,} \\  & GD & SAR & LANDSAT-8, & E & - \\  & WL & O & COSMO-SkyMed, & & \\  & WL & & & PlanetScope & \\ \hline \multirow{2}{*}{[49]} & GD & SAR & ENVISAT SAR & E and T & Qinghai–Tibet railway, China \\ \hline \multirow{2}{*}{[50]} & TZ & SAR & Sentinel-1 & E and T & Dongelu tunnel of the \\  & & & & China–Tibet railway \\ \hline \multirow{2}{*}{[51]} & TD & SAR & Sentinel-1 & E and T & Qinghai–Tibet railway, China \\  & TD & O & IKONOS & E and T & pre- and post-seismic railway curves in Dujiangyan City, China \\ \hline \multirow{2}{*}{[32]} & B & SAR & Sentinel-1A & E and T & Ganjiang Super Bridge, China \\  & & & & COSMO-SkyMed & E and T & Rochester Bridge, UK \\ \hline \multirow{2}{*}{[53]} & GD & SAR & \multicolumn{2}{c}{Sentinel-1A, COSMO-SkyMed} & E and T & Foggia, Italy \\  & & & & & \\ \hline \multirow{2}{*}{[54]} & VG & O & \multicolumn{2}{c}{LANDSAT-7,} & \multirow{2}{*}{E and T} & Beijing–Tianjin intercity \\  & & & & & high-speed railway, China \\ \hline \multirow{2}{*}{[55]} & TZ & SAR & Sentinel-1A & E and T & Changgan high-speed railway, China \\  & GD & SAR & \multicolumn{2}{c}{Sentinel-1A,} & \multirow{2}{*}{E and T} & Puglia, Italy \\  & & & & COSMO-SkyMed & \\ \hline \multirow{2}{*}{[57]} & GD & SAR & Sentinel-1 & E and T & Railway segment in Barcelona, Spain \\  & & & & & Spain \\ \hline \multirow{2}{*}{[58]} & GD & SAR & Sentinel-1 & E, T, and V & Qom–Kashan railway, Iran \\ \hline \multirow{2}{*}{[59]} & B & SAR & Cosmo-SkyMed & E and T & Railway bridge over the Voltruno \\  & & & & river at Trifilisco (Campania, Italy) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._able displacement parameters for each point in order to detect railway instability.