Urban flooding is a growing climate change-related hazard in rapidly expanding African cities, where inadequate waste management often blocks drainage systems and amplifies flood risks. This study introduces an AI-powered urban waste mapping workflow that leverages openly available aerial and street-view imagery to detect municipal solid waste at high resolution. Applied in Dar es Salaam, Tanzania, our approach reveals spatial waste patterns linked to informal settlements and socio-economic factors. Waste accumulation in waterways was found to be up to three times higher than in adjacent urban areas, highlighting critical hotspots for climate-exacerbated flooding. Unlike traditional manual mapping methods, this scalable AI approach allows city-wide monitoring and prioritization of interventions. Crucially, our collaboration with local partners ensured culturally and contextually relevant data labeling, reflecting real-world reuse practices for solid waste. The results offer actionable insights for urban planning, climate adaptation, and sustainable waste management in flood-prone urban areas.

翻译：城市内涝是快速扩张的非洲城市中不断加剧的气候变化相关灾害，不完善的废弃物管理常堵塞排水系统并放大洪水风险。本研究提出了一种基于人工智能的城市废弃物测绘流程，利用公开可用的航空与街景影像，以高分辨率检测城市固体废弃物。在坦桑尼亚达累斯萨拉姆的应用中，我们的方法揭示了与非正规住区及社会经济因素相关的空间废弃物分布模式。水道中的废弃物堆积量高达周边城区三倍，凸显了气候变化加剧洪水风险的关键热点区域。与传统人工测绘方法不同，这种可扩展的人工智能方法实现了全城范围内的监测与干预优先级排序。尤为关键的是，通过与当地合作伙伴协作，我们确保了符合文化与地域背景的数据标注，真实反映了固体废弃物的再利用实践。研究成果为洪涝多发城区的城市规划、气候适应和可持续废弃物管理提供了可操作的见解。