Projects and Publications

On this page we present our current and completed projects and keep an overview of our contributions to professional conferences and journals.




FAIR-DS is a BMBF-funded project to develop a cloud-based data space for science and research in the context of the GAIA-X cloud and the National Research Data Infrastructure (NFDI). Important goals here are (i) to identify synergies of GAIA-X and NFDI with regard to the creation of a cloud  infrastructure, (ii) to define procedural models and clarify legal issues in this regard, and (iii) to find points of contact between science and industry  in tangible example applications.

Federal Research Minister Anja Karliczek said at the project launch (translated from German):

To accelerate innovation in Germany, we need a better transfer of knowledge between research and application, and thus especially between science and industry. The exchange of data plays an increasingly important role in this: the more data from different sources across disciplines can be networked, exchanged and reused, the greater the chance that new insights will emerge from it, leading to progress in our country. We have already launched the first fundamental initiatives for this: By funding the National Research Data Infrastructure (NFDI), we are networking research data and making them more usable across all scientific disciplines. With GAIA-X, we are building a secure, trustworthy and open data and infrastructure ecosystem in Europe that meets the highest standards of digital sovereignty.

Now we are taking an ambitious step further and linking these two initiatives through the new collaborative project 'FAIR-Data Spaces'. The Federal Ministry of Education and Research is funding this with 6.3 million euros. In this way, we want to show that a common data space is feasible. In this way, a new instrument of knowledge and technology transfer is to be created in perspective, which connects data spaces from business and science and opens up previously unused potential.

In the FAIR-DS context, the Database Research Group at the University of Marburg is developing a cloud demonstrator of the Geo Engine geospatial analysis platform that uses the GAIA-X infrastructure and provides data from NFDI4BioDiversity. Geo Engine is a platform that bundles the integration and efficient processing of spatio-temporal data and intuitively provides access to the latest visualization and analysis methods. This enables research groups and companies to tap into previously untapped potential. Within FAIR-DS, Geo Engine will be made cloud-ready as a  Kubernetes service, GAIA-X specifications will be implemented, and automatic provisioning as well as a connection to data providers will be  implemented.

For the development of the open source software Geo Engine the Database Research Group is supported by Geo Engine GmbH. Our company helps with the training of the project staff as well as with the prototypical implementation of the first features of the demonstrator. Furthermore, Geo Engine GmbH leads the community development of the software, performs code reviews and develops architectural goals that make sustainable software development possible.


TerraNova is The European Landscape Learning Initiative that trains 15 PhD candidates as Early Stage Researchers (ESRs) in landscape histories and futures. TerraNova’s mission is to develop an unprecedented digital atlas of Europe compiled by an interdisciplinary group of researchers that combine human population patterns in the past, plants and disturbances, animal development, and climate change. 

Based on this atlas, TerraNova will give strategic guidelines and policy measures for politicians and landscape practitioners, prove the strength of interdisciplinary research in academia and to raise sustainable awareness on landscape reform. The ultimate mission of the research project is to inform policy makers and the general public how to cope with the current transition into the Low Carbon Society.

Geo Engine GmbH is involved in developing and deploying the Terranova Data Atlas and provides associated training, workshops and support for the Data Atlas. This enables the Terranova ESRs as well as the researchers in the project to appropriately prepare their heterogeneous data sources as well as present the results and other relevant data to internal and external Stakeholders.


This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 813904.


The DFG-funded research project RESPECT deals with tropical mountain rainforests and dry forests in southern Ecuador and their threat from climate change and land use change. Due to the great diversity and complexity of this region, there are major challenges in modeling and analysis.

Geo Engine GmbH is developing a geodata portal in a subcontract that provides, among other things, raster time series from ECOSTRESS and Sentinel-3 with vector data of coastlines, rivers and cities together with project results via web, OGC and Python interfaces.

GEO BON EBV Analyzer

GEO BON a part of GEO, the Group on Earth Observations. Within the GEO family, GEO BON represents Biodiversity, one of GEO’s nine Societal Benefit Areas. In this project, we are developing the EBV Analyzer as part of the EBV Data Portal, which makes Essential Biodiversity Variables (EBVs) available to the general public. These EBVs represent indicators of the status of and changes in global biodiversity. In the EBV Data Portal, they can be visualized on a map and analyzed over time using plots.

EXIST Research Transfer

Geo Engine is an EXIST research transfer project of the Database Research Group at the University of Marburg. The project uses research results achieved in the GFBio and Nature 4.0 projects in the field of geodata processing, visualization and deep learning.

As of September 2022, we are in our second funding phase and focused on expanding our AI/ML pipeline, scaling our cloud deployments, and refining our business model.


NFDI4Biodiversity is a consortium under the umbrella of the National Research Data Infrastructure dedicated to the collaborative use of biodiversity and environmental data. In this project, the portal builder capabilities of the Geo Engine are extended according to the requirements of the biodiversity community and concrete portals are prototyped.


The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission that aims to monitor the environment on a global scale. In the BMWK-funded project CropyHype, we are working on the automated classification of fields based on planted crop varieties in the smallholder regions of Western Kenya and the early provision of this information for downstream process chains. For this purpose, novel data from the EnMap mission will be combined with Sentinel-2 data and processed using machine learning (ML) techniques. The implementation of the data connection, pre-processing and analysis as well as the presentation and provision is carried out by the cloud service Geo Engine. The ML methods are developed by the Climate Geography and Environmental Modeling group at the University of Marburg.