mwBTFreddy: A Dataset for Flash Flood Damage Assessment in Urban Malawi
This dataset addresses the problem of flash flood damage assessment for urban planning and emergency responders in climate-vulnerable regions of Africa, but it is incremental as it applies existing methods to new data.
The paper introduces the mwBTFreddy dataset, which provides paired pre- and post-disaster satellite images and building damage annotations from Cyclone Freddy in urban Malawi to support machine learning models for damage assessment, aiming to aid in relocation, planning, and emergency response.
This paper describes the mwBTFreddy dataset, a resource developed to support flash flood damage assessment in urban Malawi, specifically focusing on the impacts of Cyclone Freddy in 2023. The dataset comprises paired pre- and post-disaster satellite images sourced from Google Earth Pro, accompanied by JSON files containing labelled building annotations with geographic coordinates and damage levels (no damage, minor, major, or destroyed). Developed by the Kuyesera AI Lab at the Malawi University of Business and Applied Sciences, this dataset is intended to facilitate the development of machine learning models tailored to building detection and damage classification in African urban contexts. It also supports flood damage visualisation and spatial analysis to inform decisions on relocation, infrastructure planning, and emergency response in climate-vulnerable regions.