UAV-assisted municipal solid waste monitoring for informed disposal decisions [research data]

• Verfasst von: Knoblauch, Steffen [VerfasserIn]
• Verfasst von: Szamek, Levi [VerfasserIn]
• Verfasst von: Wenk, Jonas [VerfasserIn]
• Verfasst von: Chazua, Iddy [VerfasserIn]
• Verfasst von: Maholi, Innocent [VerfasserIn]
• Verfasst von: Adamiak, Maciej [VerfasserIn]
• Verfasst von: Lautenbach, Sven [VerfasserIn]
• Verfasst von: Zipf, Alexander [VerfasserIn]
• Titel: UAV-assisted municipal solid waste monitoring for informed disposal decisions [research data]
• Verf.angabe: Steffen Knoblauch, Levi Szamek, Jonas Wenk, Iddy Chazua, Innocent Maholi, Maciej Adamiak, Sven Lautenbach, Alexander Zipf
• Verlagsort: Heidelberg
• Verlag: Universität
• E-Jahr: 2024
• Jahr: 2024-10-15
• Umfang: 1 Online-Ressource (5 Files)
• Fussnoten: Gefördert durch: Klaus Tschira Foundation; Deutsche Forschungsgemeinschaft (DFG): 451956976 ; Gesehen am 15.10.2024
• DOI: doi:10.11588/data/CEIWEA
• Datenträger: Online-Ressource
• Dokumenttyp: Forschungsdaten
• Dokumenttyp: Datenbank
• Sprache: eng
• Bibliogr. Hinweis: Forschungsdaten zu: Knoblauch, Steffen, 1995 - : UAV-assisted municipal solid waste monitoring for informed disposal decisions
• Sonstige Nr.: Grant number: DFG 451956976
• Sach-SW: Earth and Environmental Sciences
• K10plus-PPN: 1905683545
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

MUNICIPAL SOLID WASTE DETECTION MODEL OF CONFERENCE PAPER (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 (SDG 3, 6, 14, 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 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 0.92 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. (2024-05-17)