GeoWaste: Leveraging GIS and Machine Learning for Urban Waste Management in African Cities

Waste collection in many cities in Africa remains ineffective, with little reliable data, fragmented reporting, and static truck routes, all of which lead to increased greenhouse gas emissions and overflowing bins. We present GeoWaste, a GIS and machine learning-driven system that integrates open-source geospatial datasets, GPS-enabled collection trucks, citizen geo-tagged reports, and fill-level sensors to deliver an integrated spatio-temporal waste database. GeoWaste generates optimised routes and makes forecasts using a Random Forest regressor to predict waste volumes, heuristic solutions for routing, and clustering (K-Means, DBSCAN) to locate hotspots. A pilot was done in Yenagoa, the state capital of Bayelsa State, covering 120 bins and 8540 households. Service coverage was increased from 62% to 87%, average collection time was reduced by 32%, and truck fuel use was reduced by 28%. GeoWaste demonstrates a scalable pathway for data-driven, climate-smart urban resilience.