Articles | Volume 21, issue 2
Nat. Hazards Earth Syst. Sci., 21, 643–662, 2021
https://doi.org/10.5194/nhess-21-643-2021

Special issue: Groundbreaking technologies, big data, and innovation for...

Nat. Hazards Earth Syst. Sci., 21, 643–662, 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 et al.

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (30 Sep 2020) by Carmine Galasso
AR by Kai Schröter on behalf of the Authors (11 Nov 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (11 Nov 2020) by Carmine Galasso
RR by Anonymous Referee #3 (28 Nov 2020)
RR by Anonymous Referee #4 (18 Dec 2020)
ED: Publish subject to minor revisions (review by editor) (18 Dec 2020) by Carmine Galasso
AR by Kai Schröter on behalf of the Authors (23 Dec 2020)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (23 Dec 2020) by Carmine Galasso
AR by Kai Schröter on behalf of the Authors (28 Dec 2020)  Author's response    Manuscript
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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.
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