# The Use of Earth Observation Data for Railway Infrastructure Monitoring--A Review
## 4 Discussion and Outlook
### Current Trends and Challenges in Satellite Data Utilization for Railway Monitoring  
As already mentioned in the Introduction, and as confirmed with the above-presented overview of the literature, the published related results in using satellite data for railway infrastructure monitoring mainly consider using SAR data, and only a few publications consider using optical satellite data. A possible reason for this situation is that non-commercial optical data are not of sufficient resolution, so only commercial data can provide the needed high resolution. Such data, however, due to their price, are mainly not available for research. Another possible reason is that novel developments are created for commercial services, so there are no publications about them apart from some information on the websites of the related companies. For example, the company LiveEO [82] has developed management tools to view year-round vegetation conditions on railway assets using the Sentinel-2 optical data to calculate vegetation indices for vegetation monitoring, combined with commercial data, e.g., optical high-resolution satellite imagery from Planet Labs and Airbus. The satellite data are analyzed using advanced AI. This situation of not-broadly available literature about the use of different satellite data types could be overcome by publishing results in some related ongoing projects funded by the EU (Horizon)/EUSPA (European Union Agency for the Space Programme). For example, the authors are involved in the project SPATRA: Space-based Applications for Transport Monitoring and Management [62], within which, satellite-based monitoring of railway infrastructure with a particular focus on an EO-based system for rail track temperature estimation is under development. SPATRA EO-based system aims to enhance cost-effectiveness and expand monitoring capabilities by overcoming the limitations of established state-of-the-art methods for monitoring the rail tracks' temperature by local in situ sensors or predicting the rail temperature based on meteorological data. The main goal of monitoring the rail track temperatures is to detect zones critically exposed to extreme temperatures as well as for targeted intervention to prevent and mitigate the risk of disruption. The SPATRA system is based on novel use of the satellite thermal data and advanced image processing and machine learning techniques such as random forest algorithms to downscale Land Surface Temperature (LST) data from coarser resolutions (1-5 km) to an acceptable resolution of 30 m or finer. Specifically, the approximate daily LST product from the Sentinel-3 mission with 1 km spatial resolution is combined with the hourly LST product from MSG geostationary satellites (provided by the Copernicus Global Land Monitoring Service), paying the price of 5 km spatial resolution, to form a sufficient data basis for the proposed application, as it can face the monitoring of rail tracks in large areas. Since Sentinel-3 produces its LST product daily, the heat profile index map can be updated daily, and its calculation can be based on a predefined number of consecutive past days (e.g., 10 days). However, the above-mentioned possible spatial resolution of sub-daily imagery Sentinel 3 LST -MSG is not sufficient for rail temperature monitoring purposes. To overcome this issue, data fusion of temporally high-resolution thermal imagery with spatially high-resolution data (Sentinel-2 or Landsat 8/9) is performed. For this purpose, machine learning approaches such as random forest or statistical regression downscaling procedures that have been successfully applied in previous research activities using environmental parameters from Sentinel-2 and other non-Copernicus missions (e.g., Landsat 8/9 and MODIS) are used. Hence, the goal is to develop downscaling models for Sentinel 3-MSG LST data to 1 km spatial resolution or for Sentinel-3 LST 1 km to 150 m and lower as of 30 m if found appropriate/needed.