Articles | Volume 21, issue 10
https://doi.org/10.5194/nhess-21-3199-2021
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, Anaïs Couasnon, Marleen C. de Ruiter, Marc J. C. van den Homberg, Aklilu Teklesadik, and Hans de Moel

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Cited articles

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. 
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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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