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.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-21-643-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Feb 2021
Research article |
| 16 Feb 2021
| 16 Feb 2021
Are OpenStreetMap building data useful for flood vulnerability modelling?
Marco Cerri
Section Hydrology, GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Max Steinhausen
Section Hydrology, GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Geography Department, Humboldt-Universität zu Berlin, 12489 Berlin, Germany
Heidi Kreibich
Section Hydrology, GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Kai Schröter
CORRESPONDING AUTHOR
Section Hydrology, GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
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Cited
18 citations as recorded by crossref.
- A systematic review with bibliometric analysis of different approaches and methodologies for undertaking flood vulnerability research T. Nguyen et al. 10.1007/s40899-023-00865-8
- OpenStreetMap for multi-faceted climate risk assessments E. Mühlhofer et al. 10.1088/2515-7620/ad15ab
- Towards a Sensitivity Analysis in Seismic Risk with Probabilistic Building Exposure Models: An Application in Valparaíso, Chile Using Ancillary Open-Source Data and Parametric Ground Motions J. Gómez Zapata et al. 10.3390/ijgi11020113
- Automated identification of building features with deep learning for risk analysis F. Gouveia et al. 10.1007/s42452-024-06070-2
- Quality of crowdsourced geospatial building information: A global assessment of OpenStreetMap attributes F. Biljecki et al. 10.1016/j.buildenv.2023.110295
- Assessing the impact of climate change on fluvial flood losses in urban areas: a case study of Pamplona (Spain) E. Soriano et al. 10.1080/02626667.2023.2246452
- Residential building and sub-building level flood damage analysis using simple and complex models R. Paulik et al. 10.1007/s11069-024-06756-1
- Global building exposure model for earthquake risk assessment C. Yepes-Estrada et al. 10.1177/87552930231194048
- Evaluation of residential building damage for the July 2021 flood in Westport, New Zealand R. Paulik et al. 10.1186/s40562-024-00323-z
- Modelling national residential building exposure to flooding hazards R. Paulik et al. 10.1016/j.ijdrr.2023.103826
- Model parameter influence on probabilistic flood risk analysis R. Paulik et al. 10.1016/j.ijdrr.2023.104215
- Free Global DEMs and Flood Modelling—A Comparison Analysis for the January 2015 Flooding Event in Mocuba City (Mozambique) J. Garrote 10.3390/w14020176
- Spatial Transferability of Residential Building Damage Models between Coastal and Fluvial Flood Hazard Contexts R. Paulik et al. 10.3390/jmse11101960
- Mapping and characterising buildings for flood exposure analysis using open-source data and artificial intelligence K. Bhuyan et al. 10.1007/s11069-022-05612-4
- Evaluating the spatial application of multivariable flood damage models R. Paulik et al. 10.1111/jfr3.12934
- Leveraging data driven approaches for enhanced tsunami damage modelling: Insights from the 2011 Great East Japan event M. Di Bacco et al. 10.1016/j.envsoft.2022.105604
- Mining real estate ads and property transactions for building and amenity data acquisition X. Chen & F. Biljecki 10.1007/s44212-022-00012-2
- Roofpedia: Automatic mapping of green and solar roofs for an open roofscape registry and evaluation of urban sustainability A. Wu & F. Biljecki 10.1016/j.landurbplan.2021.104167
18 citations as recorded by crossref.
- A systematic review with bibliometric analysis of different approaches and methodologies for undertaking flood vulnerability research T. Nguyen et al. 10.1007/s40899-023-00865-8
- OpenStreetMap for multi-faceted climate risk assessments E. Mühlhofer et al. 10.1088/2515-7620/ad15ab
- Towards a Sensitivity Analysis in Seismic Risk with Probabilistic Building Exposure Models: An Application in Valparaíso, Chile Using Ancillary Open-Source Data and Parametric Ground Motions J. Gómez Zapata et al. 10.3390/ijgi11020113
- Automated identification of building features with deep learning for risk analysis F. Gouveia et al. 10.1007/s42452-024-06070-2
- Quality of crowdsourced geospatial building information: A global assessment of OpenStreetMap attributes F. Biljecki et al. 10.1016/j.buildenv.2023.110295
- Assessing the impact of climate change on fluvial flood losses in urban areas: a case study of Pamplona (Spain) E. Soriano et al. 10.1080/02626667.2023.2246452
- Residential building and sub-building level flood damage analysis using simple and complex models R. Paulik et al. 10.1007/s11069-024-06756-1
- Global building exposure model for earthquake risk assessment C. Yepes-Estrada et al. 10.1177/87552930231194048
- Evaluation of residential building damage for the July 2021 flood in Westport, New Zealand R. Paulik et al. 10.1186/s40562-024-00323-z
- Modelling national residential building exposure to flooding hazards R. Paulik et al. 10.1016/j.ijdrr.2023.103826
- Model parameter influence on probabilistic flood risk analysis R. Paulik et al. 10.1016/j.ijdrr.2023.104215
- Free Global DEMs and Flood Modelling—A Comparison Analysis for the January 2015 Flooding Event in Mocuba City (Mozambique) J. Garrote 10.3390/w14020176
- Spatial Transferability of Residential Building Damage Models between Coastal and Fluvial Flood Hazard Contexts R. Paulik et al. 10.3390/jmse11101960
- Mapping and characterising buildings for flood exposure analysis using open-source data and artificial intelligence K. Bhuyan et al. 10.1007/s11069-022-05612-4
- Evaluating the spatial application of multivariable flood damage models R. Paulik et al. 10.1111/jfr3.12934
- Leveraging data driven approaches for enhanced tsunami damage modelling: Insights from the 2011 Great East Japan event M. Di Bacco et al. 10.1016/j.envsoft.2022.105604
- Mining real estate ads and property transactions for building and amenity data acquisition X. Chen & F. Biljecki 10.1007/s44212-022-00012-2
- Roofpedia: Automatic mapping of green and solar roofs for an open roofscape registry and evaluation of urban sustainability A. Wu & F. Biljecki 10.1016/j.landurbplan.2021.104167
Latest update: 13 Dec 2024
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.
Effective flood management requires information about the potential consequences of flooding. We...
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