构建马拉维城市洪水损毁数据集，助力灾后智能评估与应急决策。
mwBTFreddy: A Dataset for Flash Flood Damage Assessment in Urban Malawi
- 基于谷歌地球专业版获取灾前灾后卫星影像，标注建筑物损毁等级。
- 包含4类损毁标签：无损、轻损、重损、摧毁，覆盖2023年弗雷迪气旋影响区。
- 专为非洲城市环境设计，适合灾害评估、遥感与气候韧性研究者使用。
本文介绍了mwBTFreddy数据集，该资源旨在支持马拉维城市地区洪水灾后损毁评估，重点针对2023年弗雷迪气旋的影响。数据集包含来自Google Earth Pro的灾前与灾后配对卫星影像，并配有JSON文件，记录了带地理坐标的建筑物标注及损毁等级（无损、轻损、重损或摧毁）。由马拉维商业与应用科学大学的Kuyesera AI实验室开发，该数据集旨在促进面向非洲城市环境的建筑物检测与损毁分类机器学习模型研发，同时支持洪水损毁可视化与空间分析，为易受气候变化影响地区的人员疏散、基础设施规划和应急响应提供决策支持。
原文摘要 · Abstract (English)
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.
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