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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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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