Drones are revolutionizing research, industry and everyday life. The Geoinformatics research group has a large number of modern drone systems at its disposal, which can be used to collect extremely high-resolution image data in terms of time and space. Possible applications range from the evaluation of 3D point clouds in urban areas to the creation of high-resolution vegetation indices for modern agriculture. The evaluation and application of drone-based remote sensing is an integral part of the research and teaching portfolio of the Geoinformatics group.

• Drones as a monitoring tool of winter sports activities
• 3D mapping of the Morteratsch glacier
• 3D point clouds Campus Grüental [1]
• 3D point clouds Campus Grüental [2]

Understanding ecological processes requires the repeated acquisition of geospatial data in monitoring programs. The geoinformatics research group develops flexible workflows for the acquisition, enrichment, and analysis of spatiotemporal data for effective and efficient ecological monitoring. For this purpose, we combine self-collected geodata with publicly available spatial data.

The great strength of Geographic Information Systems (GIS) lies in the combination of different data levels via a common spatial reference. In so-called multi-criteria analyses, we digitally map the most diverse properties of space and compute them into spatially differentiated suitability maps: Where is the best location for a new wind turbine? Where are the best cultivation sites for the superfood grain quinoa?

• CONSUS - Suitability Analysis Tool for Suitability Analyses of Agricultural Products

Ever increasing amounts of spatiotemporal information require the development of new Data Science methods. To this end, we combine methods of geoinformatics with machine learning and information visualization for the analysis and modeling of spatial processes. Applications range from the probability of accidents between cars and wildlife, to the risk of tick bites in forests, to valuation tools for sustainable investment in the financial sector.

• ASTRA project: Modeling of accident probabilities with wildlife on traffic infrastructures