An open-access deep learning model detects openly dumped dispersed municipal solid waste in crowdsourced UAV imagery from 29 regions in 10 Sub-Saharan countries and shows strong performance with patterns tied to local population and infrastructure.
AI-based Waste Mapping for Addressing Climate-Exacerbated Flood Risk
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abstract
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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cs.CV 1years
2026 1verdicts
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Open-access model for detecting openly dumped dispersed municipal solid waste from crowdsourced UAV imagery in Sub-Saharan Africa
An open-access deep learning model detects openly dumped dispersed municipal solid waste in crowdsourced UAV imagery from 29 regions in 10 Sub-Saharan countries and shows strong performance with patterns tied to local population and infrastructure.