Railway Infrastructure Maintenance Analysis using Satellite Data - Case Väylä

Advian helped Väylävirasto (Finnish Transport Infrastructure Agency) to analyze soft areas along the railway tracks, where the embankment moves more than usual. Excessive movement of the track embankment increases the need for maintenance activities in the long run, and during a renovation, such areas need to be stabilized. The project utilized InSAR PSI analysis, which measures the ground surface movements with millimeter accuracy every couple of weeks.

Finnish Transport Infrastructure Agency (FTIA) is responsible for the development and maintenance of the Finnish rail network. The aim is to keep the rail network in a good condition so that traffic is safe and efficient. The length of the Finnish railway network is about 6,000 kilometers and it has been built for more than 100 years. The network is constantly being repaired and major renovations are done as needed.

The railway network is so extensive that not all areas can be constantly monitored, such as in terms of ground subsidence. For example, clay-based soil can sink over the years, which is affected by the amount of water the soil contains. Minor ground movement is completely harmless to the track and the track is designed to withstand even strong ground movements. However, more significant subsidence may require proactive corrective actions. In the longer term, the aim is to stabilize such areas through earthworks. 

The project's goal was to utilize radar satellite data in the management of track assets and try to identify areas where subsidence occurs. The satellite-derived subsidence results were validated with historic embankment and railway maintenance data. The historic maintenance data correlated with the satellite-derived ground subsidence data and demonstrated the power of using radar satellite data for asset monitoring. 

“It was fun to see how new space technology can be concretely utilized in the traditional infrastructure industry,” Vincent Markiet, Data Scientist, Advian. 

Advian utilized ESA Sentinel 1 SAR radar satellite data in the project. The Sentinel 1 satellite orbits the polar orbit, which repeats itself exactly every 12 days. We created and analyzed a time series of a couple of years from SAR images, in which the amount of data is calculated in terabytes. InSAR utilizes the phase difference of the radar satellite signal. From the time series of regularly recurring reflections, we can identify the movement of the ground to the direction of the satellite - which is almost equivalent to the actual vertical movement of the ground. This allows detecting changes in millimeter accuracy. 

“Finnish Transport Infrastructure Agency was an open-minded and enthusiastic customer and together we were able to validate InSAR analytics with accurate track measurements and develop satellite-based soft ground analysis,” Chris Winquist, Project Manager, Advian.

We examined two different railway track sections for the project. The first one was an old section, less than 100 kilometers long. The other one was a relatively new section, build with modern methods. The differences between the track sections were striking: the results of the analysis confirmed that the old section of the track is moving significantly, while the new section of the track is very stable. We detected previously unknown soft ground areas on the old section of the track. The analysis also looked at known frost areas where frost has previously caused track movement. In these areas, we were able to demonstrate that the ground was sinking much faster than average.

“We received valuable information about new hidden soft grounds and knowledge of the use of satellite data in the railway network maintenance,” Markku Nummelin, Railway Director, Finnish Transport Infrastructure Agency.

väylä-advian

An example of soft ground analysis. Average subsidence (mm) of several points over a couple of years.

 

 

Topics: News, Location Intelligence, Transportation, GeoAI, Remote sensing, Infrastructure, Satellite Analytics

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