Articles | Volume 21, issue 2
https://doi.org/10.5194/nhess-21-643-2021
https://doi.org/10.5194/nhess-21-643-2021
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

Viewed

Total article views: 5,408 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
4,366 952 90 5,408 88 72
  • HTML: 4,366
  • PDF: 952
  • XML: 90
  • Total: 5,408
  • BibTeX: 88
  • EndNote: 72
Views and downloads (calculated since 29 Jun 2020)
Cumulative views and downloads (calculated since 29 Jun 2020)

Viewed (geographical distribution)

Total article views: 5,408 (including HTML, PDF, and XML) Thereof 5,099 with geography defined and 309 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 08 Dec 2024
Download
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.
Altmetrics
Final-revised paper
Preprint