Our department, especially our EAGLE Earth Observation students, got mentioned in the 40 year anniversary news article of DLR-EOC. Very interesting to see how Earth Observation potentials and challenges changed over these few decades and what a great power we nowaydays have at our hands.
Looking forward to another 40 years of Earth Observation research and how we can contribute to the environmental knowledge, awareness and understanding on our planet.
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From November 13 to 15, Sofia Haag and Christian Schäfer attended the AK Hydrologie workshop in Bonn, where they presented their work from the EO4CAM project. The first day featured an insightful field excursion to the Ahrtal region, led by Prof. Dr. Jürgen Herget,...
Next year marks a very special milestone for us. The Department of Remote Sensing is turning 25! Together, we’ll also celebrate 10 years of the EAGLE MSc program and 5 years of the Department of Global Urbanisation and Remote Sensing. These anniversaries represent...
Stepping into the foyer, visitors are now greeted by large, striking images that tell the story of our research through data. Each visual represents a unique scientific perspective – from the Arctic to the cultivated landscapes of Bavaria, and from forest canopies to...
Big congratulations to Sonja Maas, who successfully defended her Master thesis today on the highly relevant and increasingly pressing topic: LiDAR-Based Acquisition Strategies for Forest Management Planning in a Mature Beech Stand Supervised by Dr. Julian Fäth and...
What are the main drivers and global patterns of farmland abandonment? How can remote sensing help address the challenges of studying farmland abandonment? In what ways might abandoned farmland contribute to biodiversity conservation and carbon reduction? These...
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”.
