Our data cube activities in AgriSens Demmin 4.0 @ GIL – Conference

Our data cube activities in AgriSens Demmin 4.0 @ GIL – Conference

March 18, 2024

At the end of February, the datacube team of our EORC contributed to the GIL-Tagung, the yearly conference of the Society for Computer Science in Agriculture, Forestry and Nutrition (GIL). The event – held at the University of Hohenheim, which is one of Germany’s top destinations for agricultural sciences – gathered some 250 scientists to discuss their latest developments in the agrifood domain, including many interesting approaches using remote sensing or big data. Together with colleagues from the AgriSens DEMMIN 4.0 project, we submitted a paper outlining the system architecture of our agricultural datacube, with an emphasis on the various ways in which its potential is delivered to end users. Read it at ResearchGate!

The picture shows Christoph Friedrich, who lead the submission and presented the paper in the session, and Johannes Löw, a colleague from the AgriSens project and former EAGLE student who remains working very closely with our datacube team while pursuing his PhD in Christopher Conrad’s group at the University of Halle.

you may also like:

EORC and DLR participated at the ILUS 2025 Conference in Dresden

EORC and DLR participated at the ILUS 2025 Conference in Dresden

The International Land Use Symposium 2025 (https://ilus2025.ioer.info/) took place in Dresden, Germany from the 6th to the 7th of November. Our colleagues from our Earth Observation Research Cluster (EORC) and the Earth Observation Center (EOC) of the German Aerospace...

Visit at the Institute for Geoinformatics (IFGI) at University of Münster

Visit at the Institute for Geoinformatics (IFGI) at University of Münster

Two days ago, our PostDoc Dr. Jakob Schwalb-Willmann visited the Institute for Geoinformatics at University of Münster to give a talk at IFGI’s GI Forum titled “Can animals be used to classify land use? Employing movement-tracked animals as environmental informants using deep learning”.