AI-based Waste Mapping for Addressing Climate-Exacerbated Flood Risk

• URL: http://arxiv.org/abs/2604.18151v1
• Date: Mon, 20 Apr 2026 12:12:16 GMT
• Title: AI-based Waste Mapping for Addressing Climate-Exacerbated Flood Risk
• Abstract summary: This study introduces an AI-powered urban waste mapping workflow in Dar es Salaam, Tanzania.<n>Our approach reveals spatial waste patterns linked to informal settlements and socio-economic factors.<n>Results offer actionable insights for urban planning, climate adaptation, and sustainable waste management in flood-prone urban areas.
• Score: 35.39124330470992
• License: http://creativecommons.org/licenses/by/4.0/
• 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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