Within this section, you will find feedback collected from RUS users during or after the completion of the project they have carried out with the support of the RUS Service.

These success stories demonstrate the variety of applications that users can benefit from the RUS Service and the potential of Copernicus data for the scaling up of R&D activities based on Earth observation data.

 

“In the frame of my PhD work, I had to explore the use of Sentinel-2 satellites for land cover mapping in a semi-arid area of Niger. All operations were done on a RUS 64-bit Virtual Machine with 8 CPUs, 30 GB of RAM, a 200 GB system disk and a 5000 GB storage disk. Maximum likelihood based re-sampling, atmospheric correction, NDVI classification and map layout were performed using the pre-installed tools SNAP and QGIS, as well as my own-installed ArcGIS tool.
The RUS service was for me a perfect opportunity to benefit from a powerful computing machine offering a facilitated access to Sentinel data, dedicated processing tools and high storage capacities. The Helpdesk and its network of experts guided my work providing information on how to handle Sentinel-2 data, notably SNAP and Sen2Cor.
Thank you to the RUS service and especially to the expert team for its availability and technical support!”
A student from Institute of Ecology and Environmental Sciences (France), and Dan Dicko Dankoulodo University of Maradi (Niger)
“Competent support, powerful rapidly deployed services, secure and stable. RUS is the best scientific processing infrastructure we came across so far. RUS staff is caring and supporting, we felt welcomed and enjoy the constructive collaboration. We are processing a novel land use product using Landsat and Sentinel-2 data which is memory hungry to create (cf. http://osmlanduse.org). With a 16-cores machine, 240 GB of RAM and 10 TB of storage, RUS gave us the muscles to apply our idea on continental scale. We expect the resulting product and publication to be of high impact. One thing we do regret: Not having used RUS earlier.”
A student from the Institute of Geography, University of Heidelberg (Germany)
“Working on derivation of LAI time-series using machine learning techniques, RUS helped me scaling my processes without worrying about costs and installation of hardware. Computing capacity (32 cores, 120 GB of RAM and 10 TB of storage) was available according to my needs, not according to available budget. The access through a web browser made RUS simply accessible from anywhere while providing the comfort of a desktop interface. The Linux environment allowed easy installation of open source tools. Questions I asked the support staff were answered quick and efficient. Overall, a very recommendable service in dealing with large Copernicus datasets!”
A student from Wageningen University & Research (The Netherlands)