AI-based Waste Mapping for Addressing Climate-Exacerbated Flood Risk
For urban planners and climate adaptation efforts in flood-prone African cities, this offers a scalable monitoring tool to address waste-blocked drainage exacerbated by climate change.
This study introduces an AI-powered workflow using aerial and street-view imagery to detect municipal solid waste in Dar es Salaam, Tanzania, finding waste accumulation in waterways up to three times higher than in adjacent areas, providing actionable insights for flood risk management.
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.