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

Related authors

Assessing the impact of early warning and evacuation on human losses during the 2021 Ahr Valley flood in Germany using agent-based modelling
André Felipe Rocha Silva, Julian Cardoso Eleutério, Heiko Apel, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 25, 1501–1520, https://doi.org/10.5194/nhess-25-1501-2025,https://doi.org/10.5194/nhess-25-1501-2025, 2025
Short summary
Deciphering the drivers of direct and indirect damages to companies from an unprecedented flood event: A data-driven, multivariate probabilistic approach
Ravi Kumar Guntu, Guilherme Samprogna Mohor, Annegret H. Thieken, Meike Müller, and Heidi Kreibich
EGUsphere, https://doi.org/10.5194/egusphere-2025-1715,https://doi.org/10.5194/egusphere-2025-1715, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
FLEMOflash – Flood Loss Estimation MOdels for companies and households affected by flash floods
Apoorva Singh, Ravi Kumar Guntu, Nivedita Sairam, Kasra Rafiezadeh Shahi, Anna Buch, Melanie Fischer, Chandrika Thulaseedharan Dhanya, and Heidi Kreibich
EGUsphere, https://doi.org/10.5194/egusphere-2025-1512,https://doi.org/10.5194/egusphere-2025-1512, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Adaptive behavior of farmers under consecutive droughts results in more vulnerable farmers: a large-scale agent-based modeling analysis in the Bhima basin, India
Maurice W. M. L. Kalthof, Jens de Bruijn, Hans de Moel, Heidi Kreibich, and Jeroen C. J. H. Aerts
Nat. Hazards Earth Syst. Sci., 25, 1013–1035, https://doi.org/10.5194/nhess-25-1013-2025,https://doi.org/10.5194/nhess-25-1013-2025, 2025
Short summary
Content analysis of multi-annual time series of flood-related Twitter (X) data
Nadja Veigel, Heidi Kreibich, Jens A. de Bruijn, Jeroen C. J. H. Aerts, and Andrea Cominola
Nat. Hazards Earth Syst. Sci., 25, 879–891, https://doi.org/10.5194/nhess-25-879-2025,https://doi.org/10.5194/nhess-25-879-2025, 2025
Short summary

Related subject area

Risk Assessment, Mitigation and Adaptation Strategies, Socioeconomic and Management Aspects
Assessing the impact of early warning and evacuation on human losses during the 2021 Ahr Valley flood in Germany using agent-based modelling
André Felipe Rocha Silva, Julian Cardoso Eleutério, Heiko Apel, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 25, 1501–1520, https://doi.org/10.5194/nhess-25-1501-2025,https://doi.org/10.5194/nhess-25-1501-2025, 2025
Short summary
Adaptive behavior of farmers under consecutive droughts results in more vulnerable farmers: a large-scale agent-based modeling analysis in the Bhima basin, India
Maurice W. M. L. Kalthof, Jens de Bruijn, Hans de Moel, Heidi Kreibich, and Jeroen C. J. H. Aerts
Nat. Hazards Earth Syst. Sci., 25, 1013–1035, https://doi.org/10.5194/nhess-25-1013-2025,https://doi.org/10.5194/nhess-25-1013-2025, 2025
Short summary
Content analysis of multi-annual time series of flood-related Twitter (X) data
Nadja Veigel, Heidi Kreibich, Jens A. de Bruijn, Jeroen C. J. H. Aerts, and Andrea Cominola
Nat. Hazards Earth Syst. Sci., 25, 879–891, https://doi.org/10.5194/nhess-25-879-2025,https://doi.org/10.5194/nhess-25-879-2025, 2025
Short summary
Enhancement of state response capability and famine mitigation: a comparative analysis of two drought events in northern China during the Ming dynasty
Fangyu Tian, Yun Su, Xudong Chen, and Le Tao
Nat. Hazards Earth Syst. Sci., 25, 591–607, https://doi.org/10.5194/nhess-25-591-2025,https://doi.org/10.5194/nhess-25-591-2025, 2025
Short summary
Flood exposure of environmental assets
Gabriele Bertoli, Chiara Arrighi, and Enrica Caporali
Nat. Hazards Earth Syst. Sci., 25, 565–580, https://doi.org/10.5194/nhess-25-565-2025,https://doi.org/10.5194/nhess-25-565-2025, 2025
Short summary

Cited articles

Alfieri, L., Feyen, L., Salamon, P., Thielen, J., Bianchi, A., Dottori, F., and Burek, P.: Modelling the socio-economic impact of river floods in Europe, Nat. Hazards Earth Syst. Sci., 16, 1401–1411, https://doi.org/10.5194/nhess-16-1401-2016, 2016. a
Amadio, M., Scorzini, A. R., Carisi, F., Essenfelder, A. H., Domeneghetti, A., Mysiak, J., and Castellarin, A.: Testing empirical and synthetic flood damage models: the case of Italy, Nat. Hazards Earth Syst. Sci., 19, 661–678, https://doi.org/10.5194/nhess-19-661-2019, 2019. a
Amirebrahimi, S., Rajabifard, A., Mendis, P., and Ngo, T.: A framework for a microscale flood damage assessment and visualization for a building using BIM–GIS integration, Int. J. Digit. Earth, 9, 363–386, https://doi.org/10.1080/17538947.2015.1034201, 2016. a
Apel, H., Aronica, G. T., Kreibich, H., and Thieken, A.: Flood risk analyses–how detailed do we need to be?, Nat. Hazards, 49, 79–98, https://doi.org/10.1007/s11069-008-9277-8, 2009. a, b
Barrington-Leigh, C. and Millard-Ball, A.: The world’s user-generated road map is more than 80 % complete, Plos One, 12, 1–20, https://doi.org/10.1371/journal.pone.0180698, 2017. a, b
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.
Share
Altmetrics
Final-revised paper
Preprint