Big Earth Data Analytics


The research cluster of Big Earth Data Analytics, tailored to EO data, allows the management and presentation of vast amount of EO data and the discovery of new information that is hidden in the data and promote the value-adding combination with non-EO data streams. The cluster will research and develop technologies related to data mining, machine learning, visual spatio-temporal exploration of big geospatial and temporal data, semantic enrichment of EO data and products, fusing EO and crowd-sourced data generated from smart sensor technology and geoinformatics. In the last years, different data mining technologies have been developed to cope with the different volume, variety, velocity and veracity of space-based data in order to make the EO services and applications development more efficient and to benefit from all the information hidden within the data. The need to move geospatial data analysis, and more specifically EO data processing, into the “cloud” and to store and represent data in formats (e.g., data cubes) has been recognised by many organisations worldwide. Consequently, several organisations and initiatives worldwide have already begun or are preparing for the uptake of EO data into their Big Data infrastructures. The activities of the research cluster are presented in more detail in the following sections.

The Big Earth Data Analytics Department consists of several researchers (Postdoctoral and PhD students) and is coordinated by Mr Gunter Schreier from DLR, one of the EXCELSIOR project’s advance partners.

Gunter Schreier


This project has received funding from the Government of the Republic of Cyprus through the “Directorate General for European Programmes, Coordination and and Development”.
This project has received funding from the European Union’s “Horizon 2020 Research and Innovation Programme” under Grant Agreement No 857510”.
This project is co-funded by the Cyprus University of Technology.