EGU21-8574, updated on 04 Mar 2021
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Earth Observation Techniques for Spatial Disaggregation of Exposure Data 

Christian Geiß1, Patrick Aravena Pelizari1, Peter Priesmeier2, Angélica Rocio Soto Calderon3, Elisabeth Schoepfer1, Michael Langbein1, Torsten Riedlinger1, Hernán Santa María4,5, Juan Camilo Gómez Zapata6, Massimiliano Pittore6,7, and Hannes Taubenböck1
Christian Geiß et al.
  • 1German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Weßling-Oberpfaffenhofen, Germany (,,,,, hannes.taube
  • 2TH Köln – University of Applied Sciences, Cologne, Germany (
  • 3Technical University of Munich, Munich, Germany (
  • 4National Research Center for Integrated Natural Disaster Management (CIGIDEN), Santiago de Chile, Chile (
  • 5Department of Structural and Geotechnical Engineering, Pontifical Catholic University of Chile, Santiago de Chile, Chile (
  • 6GFZ German Research Centre for Geosciences, Seismic Hazard and Risk Dynamics, Potsdam, Germany (
  • 7EURAC Research, Institute for Earth Observation, Bolzano, Italy (

Exposure describes elements which are imperiled by natural hazards and susceptible to damage. The affiliated vulnerability characterizes the likelihood to experience damage regarding a given level of hazard intensity. Frequently, the compilation of exposure information is the costliest component (in terms of time and labor) in risk assessment. Existing data sets and models often describe exposure in an aggregated manner, e.g., by relying on statistical/census data for given administrative entities. Nowadays, earth observation techniques allow to collect spatially continuous information for large geographic areas while enabling a high geometric and temporal resolution. In parallel, modern data interpretation tools based on Artificial Intelligence concepts enable the extraction of thematic information from such data with a high accuracy and detail. Consequently, we exploit measurements from the earth observation missions TanDEM-X and Sentinel-2, which collect data on a global scale, to characterize the built environment in terms of fundamental morphologic properties, namely built-up density and height. Subsequently, we use this information to constrain existing exposure data in a spatial disaggregation approach. Thereby, we compare different methods for disaggregation and evaluate how different resolution properties of the earth observation data affect the risk assessment result. Results are presented for the city of Santiago de Chile, Chile, which is prone to natural hazards such as earthquakes. We present loss estimations and corresponding sensivity with respect to the resolution properties of the exposure data used in the model. Thereby, it can be noted how loss estimations vary substantially and that aggregated exposure information underestimates losses in our scenarios. As such, this study underlines the benefits of deploying modern earth observation technologies for refined exposure estimation and related loss estimation.

How to cite: Geiß, C., Aravena Pelizari, P., Priesmeier, P., Rocio Soto Calderon, A., Schoepfer, E., Langbein, M., Riedlinger, T., Santa María, H., Gómez Zapata, J. C., Pittore, M., and Taubenböck, H.: Earth Observation Techniques for Spatial Disaggregation of Exposure Data , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8574,, 2021.