television and radio coverage about urban measurements

television and radio coverage about urban measurements

m

June 20, 2024

Our urban research got covered by TV and radio where we had the chance to explain the relevance of urban monitoring via remote sensing methods as well as in-situ devices (in cooperation with Prof. Marco Schmidt) especially for adaptation and mitigation potential of German urban environments. The multidisciplinary team covers the fields of remote sensing, (geo)-informatics, computer science, urban planning, social geography, linguistics, digital humanities, psychology, business informatics and sports science.

Beside the press crew also the interested public approached us during this event and asked about the research, its aim and relevance. We had plenty of time to elaborate the our research and also show the spatio-temporal results.

you may also like:

A Glimpse into Our Research: Data on Display in the Foyer

A Glimpse into Our Research: Data on Display in the Foyer

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...

Successful MSc defense by Sonja Maas

Successful MSc defense by Sonja Maas

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...

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”.