My research centers on how advances in remote sensing and machine learning can help enhance understanding of environmental risks and offer relevant information for mitigation options. I combine novel generic data streams originating from earth observation and other data sources such as OpenStreetMap to produce data products useful for decision making and government agencies. Currently I’m working on the https://floodadapt.eoc.dlr.de/ project which is focused on floods and flood risks, their effect on assets and its environmental interdependencies.
Previously, I was a postdoc on the fusion of volunteer geographic information (VGI) data and remote sensing to create novel data products such as https://osmlanduse.org/ with the GIScience group at the University of Heidelberg, following my Ph.D. on remote time series analysis to support reducing emissions from deforestation and forest degradation at Wageningen University. Alongside both activities I offered my skills in various international consulting projects.
Before grad school, I worked on various topics such as validation efforts for Global Observation for Forest Cover and Land Dynamics (GOFC/GOLD), hydrological modeling and hyperspectral data processing and capturing at the University of Jena. There, I also served recently as a short term substitute assistant professor on remote sensing fundamentals.