Preprints
https://doi.org/10.5194/nhess-2020-417
https://doi.org/10.5194/nhess-2020-417

  15 Jan 2021

15 Jan 2021

Review status: this preprint is currently under review for the journal NHESS.

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 Wouters1,2, Hans de Moel1, Marleen C. de Ruiter1, Anaïs Couasnon1, Marc J. C. van den Homberg2, and Aklilu Teklesadik2 Lucas Wouters et al.
  • 1Institute for Environmental studies (IVM), Vrije Universiteit Amsterdam, De Boelelaan 1087, 1081HV Amsterdam
  • 2510 , an initiative of the Netherlands Red Cross, Anna van Saksenlaan 50, 2593 HT Den Haag

Abstract. Reliable information on building stock and its vulnerability is important for understanding societal exposure to floods. Unfortunately, developing countries have less access to and availability of this information. Therefore, calculations for flood damage assessments have to use the scarce information available, often aggregated on a national or district level. This study aims to improve current assessments of flood damage by extracting individual structural building characteristics and estimate damage based on the buildings' vulnerability. We carry out an Object-Based Image Analysis (OBIA) of high-resolution (11 cm ground sample distance) Unmanned Aerial Vehicle (UAV) imagery to outline shapes. We then use a Support Vector Machine Learning algorithm to classify the delineated buildings. We combine this information with local depth-damage curves to estimate the economic damages for three villages affected by the 2019 January river floods in the Southern Shire basin in Malawi, and compare this to a conventional approach using land use to denote exposure. The flood extent is obtained from satellite imagery (Sentinel-1), and corresponding water depths determined by combining this with elevation data. The estimated damages from the OBIA and aggregated land-use approach yield €10,140 and €15,782, respectively, highlighting the potential for detailed and local damage assessments using UAV imagery.

Lucas Wouters et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Lucas Wouters et al.

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 (€10,140) to a conventional land-use based approach (€15,782) for a specific flood event, recommendations are made for future assessments.
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