Graph-Based Geocoding and Address Generation for Africa: A QGIS-Integrated Approach Using Street Network Topology Trained on European Reference Cities

Africa remains the most under-addressed continent on the planet, with an estimated 60–70% of streets and dwellings lacking formal, standardised addresses. This deficit has profound consequences for governance, emergency response, e-commerce, logistics, and the daily lives of over 1.4 billion people. We present a hybrid graph-theoretic and machine learning framework for automated address generation, trained on high-quality open data from three European cities (Stuttgart, Paris, and Bern) and progressively adapted to seven African cities (Kigali, Dakar, and Kampala as training cities; Dar es Salaam, Nairobi, Kinshasa, and Lagos as unseen test cities). The framework models street networks as primal and dual graphs, applies the Intersection Continuity Negotiation algorithm for stroke detection, and deploys four supervised classifiers for connectivity error detection, road type classification, building-to-street assignment, and address quality scoring. Building footprints are fused from three complementary sources (OpenStreetMap, Google Open Buildings, Microsoft Building Footprints) and augmented with SRTM elevation data and the World Settlement Footprint 3D (WSF 3D) dataset. Across ten study cities, the pipeline produces 3.2 million merged building footprints, 467,000 street edges, and 880,464 quality-scored addresses, and a five-version iterative improvement cycle reduces the EU–Africa address quality gap by 54.2%. To validate African address outputs independently, we construct a cross-validation database of 1000 verifiable addresses of known organisations across the seven African cities; cross-validation achieves a geometric match rate of 79.6% within 100 m and 83.5% within 150 m. The system is implemented as an open-source QGIS plugin and ArcGIS Toolbox.