mwBTFreddy: A Dataset for Flash Flood Damage Assessment in Urban Malawi
Chapuma, Evelyn, Mengezi, Grey, Msasa, Lewis, Taylor, Amelia
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
May-5-2025
- Country:
- Africa
- Madagascar (0.04)
- Malawi
- Central Region > Lilongwe District
- Lilongwe (0.04)
- Northern Region > Mzimba District
- Mzuzu (0.04)
- Southern Region > Blantyre District
- Blantyre (0.05)
- Central Region > Lilongwe District
- Mozambique (0.04)
- Southern Africa (0.04)
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- California > Alameda County > Berkeley (0.04)
- Africa
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