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

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Final revised paper (published on 16 Feb 2021)
Preprint (discussion started on 29 Jun 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review of "Are new open building data useful for flood vulnerability modelling?" by Cerri et al.', Anonymous Referee #1, 20 Jul 2020
- AC1: 'Response to comments of reviever #1', Kai Schröter, 18 Sep 2020
RC2: 'Review of paper', Anonymous Referee #2, 27 Aug 2020
- AC2: 'Response to comments of reviever #2', Kai Schröter, 18 Sep 2020

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

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.