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arXiv Machine Learning By Steffen Knoblauch, Ram Kumar Muthusamy, Luis M. A. Bettencourt, Costas Velis, Pierre Chrzanowski, Edward Charles Anderson, Pete Masters, Innocent Maholi, Antonio Inguane, Levi Szamek, Alexander Zipf

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

Read the original on arXiv Machine Learning →

The paper presents an open‑access deep learning model that automatically detects openly dumped municipal solid waste using crowdsourced UAV imagery across 29 regions in 10 Sub‑Saharan African countries. Trained on manually annotated image tiles, the model shows excellent performance and reveals heterogeneous waste accumulation patterns, from localized hotspots along waterways to dispersed litter in urban areas. The study links waste accumulation most strongly to population density and lack of local infrastructure, underscoring the need for fine‑scale data to understand localized waste dynamics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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