Doctoral Researcher

Franz Schug is a doctoral researcher at IRI THESys and the Earth Observation Lab at Humboldt-Universität zu Berlin. Since 2018, he is pursuing his PhD studies on Earth Observation methods for anthropogenic material stocks and population mapping in close collaboration with the Institute of Social Ecology at the University of Natural Resources and Life Sciences Vienna. Previous to his PhD studies, he graduated in Physical Geography of Human-Environment Systems from Humboldt-Universität zu Berlin.

Research Interests

  • Earth Observation for quantitative urban monitoring
  • Urban growth processes and patterns
  • Spatial-temporal data modelling and visualization

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

Haberl, H.; Wiedenhofer, D.; Schug, F.; Frantz, D.; Virág, D.; Plutzar, C.; Gruhler, K.; Lederer, J.; Schiller, G.; Fishman, T.; Lanau ,M.; Gattringer, A.; Kemper, T.; Liu, G.; Tanikawa, H.; van der Linden, S.; Hostert, P. (2021): High-Resolution Maps of Material Stocks in Buildings and Infrastructures in Austria and Germany. Environmental Science & Technology, doi: 10.1021/acs.est.0c05642

Frantz, D.; Schug, F.; Okujeni, A.; Navacchi, C.; Wagner, W.; van der Linden, S.; Hostert, P. (2021): National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment, vol. 252. doi: 10.1016/j.rse.2020.112128

Wellmann, T.; Lausch, A.; Andersson, E.; Knapp, S.; Cortinovis, C.; Jache, J.; Scheuer, S.; Kremer, P.; Mascarenhas, A.; Kraemer, R.; Haase, A.;  Schug, F.; Haase, D. (2020): Remote Sensing in urban planning: Contributions towards ecologically sound policies? Landscape and Urban Planning, vol. 204. doi:

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

Wellmann, T.; Schug, F.; Haase, D.; Pflugmacher, D.; van der Linden, S. (2020): Green growth? On the relation between popoulation density, land use and vegetation cover fractions in a city using a 30-years Landsat time series. Landscape and Urban Planning, vol. 202. doi: 10.1016/j.landurbplan.2020.103857

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 Lnadsat time series. Remote Sensing of Environment, vol. 210, 218-227. doi: 10.1016/j.rse.2018.03.022

Social Media

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