AI-Empowered Urban Digital Twins: Integrating Geospatial and Legal Data for Spatial Planning and Governance

Abstract. Urban planning increasingly requires accessible ways to connect regulatory frameworks with clear spatial visualisation and machine-assisted guidance. However, the integration of legal and geospatial information into platforms that support decision-making, communication, and participation remains limited. In particular, local authorities lack an operational way to translate legal and regulatory provisions into spatially explicit permit systems, and existing Urban Digital Twin architectures rarely formalise this legal-geospatial link. This paper uses Urban Digital Twins (UDTs) and Artificial Intelligence (AI), with a focus on generative AI, for smart, participatory, and responsible spatial planning. It proposes a conceptual framework for an AI-empowered Urban Digital Twin that integrates legal and 2D/3D geospatial data in both static and dynamic forms. Its aim is to support decision-making, information provision, and participation in spatial planning processes. The framework comprises five layers: data acquisition and harmonisation, data modelling, analytics and simulation, visualisation and interaction, and governance and decision support. Each of them are supported by AI models, and is illustrated through four case studies developed with the Province of Utrecht. The framework is supported by theoretical and methodological reflections and by experimental case studies. We conclude that integrating legal and geospatial data through AI-empowered UDTs strengthens spatial planning practices, improves regulatory interpretation, supports governance processes, and enables more informed and transparent decisions, provided that current limitations in AI reliability, explainability, and human oversight are explicitly addressed.