Franz Schug


(608) 265-9219

121 Russell Labs
1630 Linden Drive
Madison, WI 53706

Franz Schug


2021: Ph.D., Earth Observation Lab, Geography Department, Humboldt-Universität zu Berlin, Germany
2017: M.Sc., Physical Geography, Humboldt-Universität zu Berlin, Germany
2014: B.Sc., Geography, Humboldt-Universität zu Berlin, Germany
2011: B.A., Business Administration, Univ. de Lorraine, France / HTW Saar, Germany

Curriculum Vitae (PDF)

Research interests

My research interest is in using Earth Observation data and methods for characterizing human settlements, and quantifying their impact on surrounding and remote areas.

My previous research includes, but is not limited to, quantifying urban expansion and densification, population estimation, and quantifying parameters and dynamics of the socio-economic metabolism with remote sensing data.

As part of the SILVIS lab, I will contribute to a better understanding of patterns and processes of global hotspots of the Wildland Urban Interface. These areas of immediate human-environmental proximity are focus areas for potential conflict such as wildfire risk, habitat and biodiversity loss, or the spread of zoonotic diseases, while the global distribution, emergence, and specific impacts of these areas is yet unclear.

Personal interests

I enjoy walking, hiking, and exploring places both in nature (lakes and forests) and in cities (cafés and breweries), cooking, watching plants grow (not literally), supporting my favorite (and any other) soccer team, playing videogames and boardgames, and having cats around.

Where I'm From

Selected Publications

Schug, F.; Frantz, D.; van der Linden, S.; Hostert, P. (2021): Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLOS ONE, 16(3). doi: 10.1371/journal.pone.0249044

Schug, F.; Frantz, D.; Okujeni, A.; van der Linden, S.; Hostert, P. (2020): Mapping urban-rural gradients of settlements and vegetation at national scale using Sentinel-2 spectral-temporal metrics and regression-based unmixing with synthetic training data. Remote Sensing of Environment, vol. 246. doi: 10.1016/j.rse.2020.111810

Schug, F.; Okujeni, A.; Hauer, J.; Hostert, P.; Nielsen, J. Ø.; van der Linden, S. (2018): Mapping patterns of urban development in Ouagadougou, Burkina Faso, using machine learning regression modeling with bi-seasonal Landsat time series. Remote Sensing of Environment, vol. 210, 218-227. doi: 10.1016/j.rse.2018.03.022