A Scalable, Interoperable Reference Architecture for AI-Enabled Urban Digital Twins: From Geospatial Data Requirements to Operational Deployment

Urban Digital Twins (UDTs) increasingly combine heterogeneous geospatial data, real-time streams, analytics, and artificial intelligence (AI) to support urban governance, yet their reliability depends on how data quality, semantics, provenance, and uncertainty are handled architecturally. This study proposes a prescriptive, technology-agnostic reference architecture for AI-enabled UDT platforms covering GIS, BIM, IoT streams, imagery, and regulatory documents. The architecture combines presentation, logic, persistence, and cross-cutting support layers with reusable patterns for semantic mediation, data-type-specific persistence, spatiotemporal processing, scalable 3D visualisation, and explicit data-quality and provenance control. A four-stage methodology links stakeholder requirements to architectural patterns and validates their operational feasibility using the AQARI national real estate digital twin in Bahrain. The implementation demonstrates integration of spatial, transactional, sensor, 3D, and regulatory data and supports building management and zoning-compliance use cases. Operational tests show sub-second parcel-query response under typical conditions and scalable real-time ingestion. The results indicate that trustworthy AI-enabled UDTs require data-quality controls and interoperability mechanisms to be treated as architectural concerns rather than downstream application tasks. The proposed architecture provides reusable guidance for scalable spatial intelligence and digital-twin deployment, while quantitative uncertainty propagation and independent validation of AI models remain priorities for future work.