GeoMultiSens – Scalable Analysis of Big Remote Sensing Data

GeoMultiSens is an interdisciplinary research project that aims to give users a global view of earth surface processes. To this end, GeoMultiSens develops novel scalable technologies to integrate and analyse data from various remote sensing missions. GeoMultiSens bundles the expertise of various computer science and remote sensing research institutes: (1) the remote sensing group at the German Research Centre for Geosciences (GFZ), which develops novel algorithms for the integration of different satellite systems into a common sensor, (2) the Zuse Institute Berlin (ZIB), which is building a data management system that can process petabyte data in a parallel and failure resistant manner, and (3) the remote sensing and computer science groups at the Humboldt University of Berlin, who are adapting remote sensing algorithms to a parallel analysis environment to enable rapid information extraction. (4) The Geoinformatics group at GFZ will develop interactive tools for the exploration and evaluation of the extracted information.

Principal investigators
Hostert, Patrick Prof. Dr. (Details) (Geomatics)

Participating organisational units of HU Berlin


Duration of project
Start date: 09/2014
End date: 12/2017

Research Areas
Geodesy, Photogrammetry, Remote Sensing, Geoinformatics, Cartography

Research Areas
Big Data

Scheffler, D., Hollstein, A., Diedrich, H., Segl, K., Hostert, P. (2017): AROSICS: An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data. - Remote Sensing, 9, 7.

Sips, M., Dransch, D., Eggert, D., Freytag, J.-C., Hollstein, A., Hostert, P., Peters, M., Pflugmacher, D., Rabe, A., Reinefeld, A., Scheffler, D., Schintke, F., Segl, K., Seibert, F., Taeschner, J.(2018): GeoMultiSens: Scalable Multi-Sensor Analysis Platform for Remote Sensing Data, Potsdam : GFZ German Research Centre for Geosciences.

Pflugmacher, D., Rabe, A., Peters, M., Hostert, P., 2019. Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey. Remote Sens. Environ. 221, 583–595.

Last updated on 2022-08-09 at 23:08