# The Use of Earth Observation Data for Railway Infrastructure Monitoring--A Review
## 3 EO-Based Railway Infrastructure Monitoring: Literature Review
### Comparing Methods in Terms of Accuracy, Efficiency (Cost) and Scalability  
The need to monitor infrastructure on railway network scale brings to the forefront the use of satellite imagery, although traditional monitoring techniques, such as on-site installed sensors, despite the ability to continuously and reliably provide quality data with adequate accuracy [58], can be time-consuming, labor-intensive, and costly [21; 43; 53; 58]. Likewise, given that railway tracks are in near-continuous use, finding a time frame for periodical geodetic measurements and inspections can be challenging [22]. In addition to offering the broadest data coverage (at network level), the highest daily productivity of over 100 km for monitoring of railway infrastructures, high temporal frequency, and data acquisition and processing benefits [79], the use of satellite optical and SAR data is also advantageous in situations where in situ measurements are in question due to technical or safety issues. In addition to multiple advantages, there are also disadvantages and limitations that accompany satellite imagery technology for specific applications in railway infrastructure monitoring. Challenges remain in achieving high temporal resolution for EO data, especially for rapid changes such as flash floods or seismic events. Furthermore, Landsat satellite imagery is inexpensive and useful at a large scale but lacks the higher spatial resolution preferred for localized investigations [80], although high-resolution commercial satellites proved higher spatial resolution [21]. As argued by [PERSON] et al. [53], each technology is usually suitable for a single specific application while having limited effectiveness for other applications. However, regarding the use of satellite imagery, there is not much research or data related to validation and comparative analysis with the results obtained with other techniques. This can be attributed to the fact that it is difficult to obtain simultaneously different measurement data over long periods [43], especially in the case of long railway track sections [21]. As noted by [PERSON] et al. [21], the availability of accurate validation data represents a significant challenge in using satellite measurements. In the context of railway infrastructure monitoring, only a few research studies made a comparative analysis of satellite imagery measurements with on-site measurements and GNSS. [PERSON] et al. [43] performed a comparative analysis of the accuracy of results obtained from TerraSAR-X satellite imagery and on-site measurements in railway infrastructure deformation monitoring. The actual on-site level measurements and PS-InSAR results showed an almost identical trend, with a difference of up to 3 mm at each measurement point for final displacement values. [PERSON] et al. [22], when comparing measurements from the TerraSAR-Xsatellite and measurements from a survey train over a 1 km segment, observed that the large railway track uplift area corresponds with a considerable normal subsidence. The mentioned authors concluded that satellite measurements of railway tracks can estimate displacement time series with millimeter precision. [PERSON] et al. [PERSON] et al. (2018) used TerraSAR-X satellite data for detection of railway track anomalies. The validation of satellite measurements with chord-based measurements, as a reference (standard) method, showed that both methods yield qualitatively similar but not always identical detection results. In the conclusion, the approach based on satellite measurements for track anomaly detection was found cost-effective, while providing useful data for current state-of-the-art methods. [PERSON] et al. [PERSON] et al. (2018) used Sentinel-1 imagery and the New Small Baseline Subset (NSBAS) algorithm for the assessment of ground subsidence along the railway in the Kashan plain in Iran. The results of InSAR displacement measurement time series and GPS showed high correlations.