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

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.