Open-access model for detecting openly dumped dispersed municipal solid waste from crowdsourced UAV imagery in Sub-Saharan Africa

2026-05-04

AI Summary

This study addresses the challenges of urban solid waste management in sub-Saharan Africa, where decentralized informal dumping and a lack of high-resolution monitoring data hinder effective intervention. To tackle this gap, we develop and openly release the first deep learning model specifically designed for waste detection in this region. Trained on crowdsourced drone imagery and manually annotated image tiles, the model demonstrates robust cross-environment generalization across 29 sites in 10 countries, accurately identifying open-air waste deposits. Integrated geospatial analysis reveals strong associations between waste hotspots and both population density and inadequate infrastructure, uncovering heterogeneous spatial patterns ranging from riverine accumulations to dispersed urban litter. Designed for immediate deployment, the model enables local institutions—even those without technical expertise—to conduct fine-grained monitoring, thereby supporting targeted waste management interventions.

Abstract

Managing municipal solid waste in rapidly urbanizing Sub-Saharan Africa remains challenging due to dispersed informal dumping and limited high-resolution datasets for spatial monitoring. We present an open-access deep learning model for automated detection of openly dumped dispersed solid waste via crowdsourced UAV imagery, trained and evaluated across 29 regions in 10 countries, encompassing diverse environmental contexts. A deep learning model trained on manually annotated image tiles achieved excellent performance in detecting openly dumped dispersed solid waste across all study regions. Predicted distributions reveal heterogeneous accumulation patterns, ranging from localized hotspots - often along waterways, where waste can exacerbate flood and public health risks - to more dispersed litter across urban areas. Waste accumulation is most strongly associated with population density and indicators of lack of local infrastructure access, whereas its relationship with broader measures of regional development is weaker, highlighting the importance of fine-scale data for understanding localized waste dynamics. By releasing the model, this study provides a ready-to-use tool for UAV imagery collected by municipalities and local mapping communities, enabling openly dumped dispersed solid waste monitoring without extensive technical expertise. This approach empowers local practitioners to convert UAV imagery into actionable insights, supporting targeted interventions and improved municipal solid waste management across Sub-Saharan Africa.

Problem

• municipal solid waste
• open dumping
• Sub-Saharan Africa
• spatial monitoring
• waste management

Innovation

• open-access deep learning
• crowdsourced UAV imagery
• dispersed solid waste detection
• municipal solid waste monitoring
• Sub-Saharan Africa