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

2026-04-20

AI Summary

Rapid urbanization and climate change are intensifying flood risks in African cities, with solid waste blockage of drainage systems serving as a critical contributing factor. This study proposes an AI-driven, high-resolution framework for identifying waste accumulation by integrating publicly available aerial and street-level imagery, computer vision, and geospatial modeling. Through collaborative, context-sensitive data annotation grounded in local knowledge, the approach uniquely bridges artificial intelligence with community insights to accurately detect waste hotspots in informal settlements and along waterways. Applied in Dar es Salaam, the framework reveals that waste accumulation in waterways is three times higher than in surrounding urban areas, effectively pinpointing high-risk flood zones and providing actionable intelligence for climate-resilient urban planning.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Humans and AI: Crowd Sourcing and Human Computation
• Machine Learning: Efficient ML / Green AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• urban flooding
• waste management
• climate change
• solid waste
• drainage blockage

Innovation

• AI-powered waste mapping
• urban flooding
• climate adaptation
• high-resolution imagery
• context-aware data labeling