The International Conference on Geographical Information Systems Theory, Applications and Management aims at creating a meeting point of researchers and practitioners that address new challenges in geo-spatial data sensing, observation, representation, processing, visualization, sharing and managing, in all aspects concerning both information communication and technologies (ICT) as well as management information systems and knowledge-based systems. The conference includes original contributions of either practical or theoretical nature, presenting research or applications, of specialized or interdisciplinary nature, addressing different aspects of geographic information systems and technologies.
Talk: Urban Waste Indicator Derived from Street View Combined with UAV Imagery: Actionable Insights for Dar es Salaam
Spatial Analysis and Integration, May 22nd, 12:00 – 13:30
Levi Szamek
Mismanaged solid waste increasingly threatens human health and worsens flooding by clogging drainage systems, which is an issue in cities like Dar es Salaam, where waste management lags behind urban growth. However, spatial data on informal dumping is scarce. This study introduces the first city-scale method for detecting outdoor solid waste using image classification with 360° Street View Imagery (SVI), supplemented by detections from unmanned aerial vehicle (UAV) imagery. Two YOLOv11m classification models were trained based on openly available SVI data: one for trash bags and one for waste piles. These were combined with UAV detections into an urban waste indicator, which visually revealed spatial patterns related to informality, population density, and socio-economic differences. The waste detections were integrated with drainage infrastructure data to evaluate flood risks linked to solid waste accumulation. The SVI models achieved high performance, with F1-scores of 0.86 and 0.98, for trash bags and waste piles respectively. Spatial overlap between SVI localisations and UAV detections was limited to just 0.5% of the area, each modality covered different parts of the environment. UAV imagery detected waste both on streets and in off-road areas inaccessible to SVI, while SVI captured street-level waste even when UAV views were obstructed. This highlights the need for a multimodal approach. Results show that especially the Msimbazi River catchment face elevated clogging and flooding risk due to accumulated waste at critical drainage nodes. The presented multi-modal workflow bridges critical data gaps, making urban waste management more actionable and resilient to flooding risks.
Related works:
Knoblauch, S., Szamek, L., Chazua, I., Adamu, B., Maholi, I., & Zipf, A. (2025). AI-based Waste Mapping for Addressing Climate-Exacerbated Flood Risk. NeurIPS 2025 Workshop on Tackling Climate Change with Machine Learning. AI-based Waste Mapping for Addressing Climate-Exacerbated Flood Risk
Knoblauch, S., Szamek, L., Wenk, J., Chazua, I., Maholi, I., Adamiak, M., Lautenbach, S., & Zipf, A. (2024). UAV-Assisted Municipal Solid Waste Monitoring for Informed Disposal Decisions. Proceedings of the 2024 International Conference on Information Technology for Social Good, 105–113. https://doi.org/10.1145/3677525.3678649