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

TL;DR

This paper presents an AI-driven method using aerial and street-view imagery to map urban waste, identifying hotspots linked to flood risks in African cities, enabling scalable monitoring and targeted interventions.

Contribution

Introduces a novel AI-based waste mapping workflow that leverages open imagery for high-resolution detection, applicable for urban flood risk mitigation in developing cities.

Findings

Waste hotspots are up to three times higher in waterways than in other urban areas.

The AI approach enables city-wide waste monitoring, surpassing manual methods.

Collaboration with local partners ensures culturally relevant data labeling.

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.