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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2505.01242 |
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| _version_ | 1866915270435536896 |
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| author | Chapuma, Evelyn Mengezi, Grey Msasa, Lewis Taylor, Amelia |
| author_facet | Chapuma, Evelyn Mengezi, Grey Msasa, Lewis Taylor, Amelia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01242 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | mwBTFreddy: A Dataset for Flash Flood Damage Assessment in Urban Malawi Chapuma, Evelyn Mengezi, Grey Msasa, Lewis Taylor, Amelia Machine Learning 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. |
| title | mwBTFreddy: A Dataset for Flash Flood Damage Assessment in Urban Malawi |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.01242 |