UAV-assisted municipal solid waste monitoring for informed disposal decisions
The population growth and urbanisation trend in Africa has exacerbated municipal solid waste (MSW) generation, posing significant environmental pollution and health hazards - effecting the United Nations Sustainable Development Goals 3, 6, 14 and 15. Addressing this issue necessitates efficient wast...
Gespeichert in:
| Hauptverfasser: | , , , , , , , |
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| Dokumenttyp: | Kapitel/Artikel Konferenzschrift |
| Sprache: | Englisch |
| Veröffentlicht: |
04 September 2024
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| In: |
GoodIT '24
Year: 2024, Pages: 105-113 |
| DOI: | 10.1145/3677525.3678649 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1145/3677525.3678649
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| Verfasserangaben: | Steffen Knoblauch, Levi Szamek, Jonas Wenk, Iddy Chazua, Innocent Maholi, Maciej Adamiak, Sven Lautenbach, Alexander Zipf |
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| 520 | |a The population growth and urbanisation trend in Africa has exacerbated municipal solid waste (MSW) generation, posing significant environmental pollution and health hazards - effecting the United Nations Sustainable Development Goals 3, 6, 14 and 15. Addressing this issue necessitates efficient waste management strategies, underpinned by accurate waste detection and mapping methodologies. This study introduces a fine-tuned MSW detection model tailored for unmanned aerial vehicle (UAV) imagery. The model’s efficacy was assessed within the Msimbazi delta in Dar es Salaam, Tanzania. Evaluation on an independent test dataset yielded an F1 score of 0.917 across all MSW instances. The generated MSW pile map revealed a threefold higher contamination level in the Msimbazi river bed compared to surrounding areas. The deployment of the fine-tuned model enables local authorities to generate regular MSW distribution maps based on UAV imagery, facilitating targeted waste disposal interventions and mitigating future risks associated with flooding, water contamination, or vector-borne diseases. | ||
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