Characterising housing stock vulnerability to floods by combining UAV, Mapillary and survey data - A case study for the Karonga district in Malawi

Abstract

To accurately identify the most vulnerable areas to floods, physical (e.g., building material) and social (e.g., education, health, income of households) housing stock information is required. However, in developing countries, this information is often unreliable, unavailable or inaccessible, and manual data collection is time-consuming. This can lead to difficulties for humanitarians or policymakers in implementing appropriate disaster risk reduction and response interventions. Therefore, there is a need for the development of alternative approaches to data collection and analysis. An alternative approach to on-site vulnerability assessment is to extract physical vulnerability characteristics, such as land use type or rooftop material, from satellite or Unmanned Aerial Vehicle (UAV) imagery. However, it is often not possible to extract other social or physical vulnerability information on the household level from solely from remote sensing data. This research develops an approach for integrating multiple data sources into a Geographic Information System to improve the completeness of data on different vulnerability indicators. This approach is applied to the housing stock of the Karonga district in Malawi. An Object-Based Image Analysis of UAV imagery is combined with a machine learning analysis of Mapillary data to enable remote identification of both rooftop and wall material. Depth-damage curves were created to describe the flood impact on the housing stock for different categories of physical vulnerability (such as building material) and levels of inundation. Moreover, local survey data is used for the creation of a social vulnerability index. Combined, the datasets represent the spatial distribution of housing stock vulnerability for multiple flood scenarios. This approach is useful in situations in which proactive risk analyses must be carried out or local-scale interventions, such as building strengthening- or flood awareness projects, have to be implemented. Finally, recommendations are given for scaling the methodology to areas where only lower resolution data is available.