用AI分析航拍图像，识别城市垃圾堆积，助力非洲洪水易发区防灾。
AI-based Waste Mapping for Addressing Climate-Exacerbated Flood Risk
- 基于公开航拍与街景图，用AI高精度识别垃圾分布。
- 达累斯萨拉姆水道垃圾量是周边城区的三倍，成洪水高风险区。
- 结合本地实际标注数据，适合城市规划与气候适应决策者。
城市内涝是快速扩张的非洲城市面临的日益严重的气候相关灾害，不良废物管理常堵塞排水系统并加剧洪涝风险。本研究提出一种基于AI的城市废物测绘工作流，利用公开可获取的航空影像与街景图像，在高分辨率下检测市政固体废物。在坦桑尼亚达累斯萨拉姆的应用表明，废物分布与非正式住区及社会经济因素密切相关。水道中的垃圾积累量高达相邻城区的三倍，凸显出气候加剧洪涝的关键热点。相比传统人工测绘，该可扩展的AI方法支持全城监测与干预优先级排序。尤为重要的是，与当地伙伴合作确保了数据标注的文化与情境相关性，真实反映了固体废物的再利用实践。研究成果为易涝城市的都市规划、气候适应和可持续废物管理提供了可操作的洞见。
原文摘要 · Abstract (English)
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.
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