Are OpenStreetMap building data useful for flood vulnerability modelling?

Cerri, Marco; Steinhausen, Max; Kreibich, Heidi; Schröter, Kai

doi:https://doi.org/10.5194/nhess-21-643-2021

Articles | Volume 21, issue 2

https://doi.org/10.5194/nhess-21-643-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Groundbreaking technologies, big data, and innovation for...

https://doi.org/10.5194/nhess-21-643-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 21, issue 2

Research article

|

16 Feb 2021

Research article |

| 16 Feb 2021

Are OpenStreetMap building data useful for flood vulnerability modelling?

Marco Cerri, Max Steinhausen, Heidi Kreibich, and Kai Schröter

Data sets

Reshaping Data with the reshape Package H. Wickham https://www.jstatsoft.org/article/view/v021i12

gdalUtilities: Wrappers for ``GDAL'' Utilities Executables J. O'Brien https://CRAN.R-project.org/package=gdalUtilities

rpostgis: linking R with a PostGIS spatial database D. Bucklin and M. Basille https://journal.r-project.org/archive/2018/RJ-2018-025/index.html

rgdal: Bindings for the ``Geospatial'' Data Abstraction Librar R. Bivand, T. Keitt, and B. Rowlingson https://CRAN.R-project.org/package=rgdal

raster: Geographic Data Analysis and Modeling R. J. Hijmans https://CRAN.R-project.org/package=raster

RPostgreSQL: R Interface to the ``PostgreSQL'' Database System J. Conway, D. Eddelbuettel, T. Nishiyama, S. K. Prayaga, and N. Tiffin https://cran.r-project.org/web/packages/RPostgreSQL/index.html

Welcome to the tidyverse H. Wickham, M. Averick, J. Bryan, W. Chang, L. D. McGowan, R. François, G. Grolemund, A. Hayes, L. Henry, J. Hester, M. Kuhn, T. L. Pedersen, E. Miller, S. M. Bache, K. Müller, J. Ooms, D. Robinson, D. P. Seidel, V. Spinu, K. Takahashi, D. Vaughan, C. Wilke, K. Woo, and H. Yutani https://doi.org/10.21105/joss.01686

Classification and Regression by randomForest A. Liaw and M. Wiener https://cran.r-project.org/doc/Rnews/Rnews_2002-3.pdf

Short summary

Effective flood management requires information about the potential consequences of flooding. We show how openly accessible data from OpenStreetMap can support the estimation of flood damage for residential buildings. Working with methods of machine learning, the building geometry is used to predict flood damage in combination with information about inundation depth. Our approach makes it easier to transfer models to regions where no detailed data of flood impacts have been observed yet.