Articles | Volume 21, issue 10
Nat. Hazards Earth Syst. Sci., 21, 3199–3218, 2021
https://doi.org/10.5194/nhess-21-3199-2021
Nat. Hazards Earth Syst. Sci., 21, 3199–3218, 2021
https://doi.org/10.5194/nhess-21-3199-2021
Research article
27 Oct 2021
Research article | 27 Oct 2021

Improving flood damage assessments in data-scarce areas by retrieval of building characteristics through UAV image segmentation and machine learning – a case study of the 2019 floods in southern Malawi

Lucas Wouters et al.

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Ai, J., Zhang, C., Chen, L., and Li, D.: Mapping Annual Land Use and Land Cover Changes in the Yangtze Estuary Region Using an Object-Based Classification Framework and Landsat Time Series Data, Sustainability, 12, 659, https://doi.org/10.3390/su12020659, 2020. 
Alam, A., Bhat, M. S., Farooq, H., Ahmad, B., Ahmad, S., and Sheikh, A. H.: Flood risk assessment of Srinagar city in Jammu and Kashmir, India, International Journal of Disaster Resilience in the Built Environment, 9, 114–129, https://doi.org/10.1108/IJDRBE-02-2017-0012, 2018. 
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. 
Belgiu, M. and Draguţ, L.: Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery, ISPRS J. Photogramm., 96, 67–75, https://doi.org/10.1016/j.isprsjprs.2014.07.002, 2014. 
Blanco-Vogt, Á., Haala, N., and Schanze, J.: Building parameters extraction from remote-sensing data and GIS analysis for the derivation of a building taxonomy of settlements – a contribution to flood building susceptibility assessment, International Journal of Image and Data Fusion, 6, 22–41, https://doi.org/10.1080/19479832.2014.926296, 2015. 
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Short summary
This research introduces a novel approach to estimate flood damage in Malawi by applying a machine learning model to UAV imagery. We think that the development of such a model is an essential step to enable the swift allocation of resources for recovery by humanitarian decision-makers. By comparing this method (EUR 10 140) to a conventional land-use-based approach (EUR 15 782) for a specific flood event, recommendations are made for future assessments.
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