Urban Digital Twins Empowered by AI and Dataspaces

Dear Colleagues,

The digital transformation of cities is accelerating, driven by the proliferation of geospatial data, advancements in artificial intelligence (AI), and the emergence of federated data ecosystems, such as data spaces. Urban Digital Twins (UDTs) have recently emerged as a key paradigm in this transformation. UDTs are dynamic, data-driven, and continuously updated digital representations of physical urban environments. They enable real-time monitoring, simulation, and predictive analysis to support urban planning, sustainable development, crisis management, and citizen engagement.

While the concept of digital twins originated in industrial engineering, its application in urban contexts presents new challenges and opportunities. Cities are highly complex socio-technical systems characterised by heterogeneous data sources, from satellite and sensor networks to administrative records and participatory data. Integrating these sources into coherent, interoperable UDTs requires novel approaches to data governance, semantic interoperability, and real-time processing.

Recent advances in AI offer powerful tools for automating data fusion, pattern recognition, predictive modelling, and decision support in UDTs. At the same time, emerging data-sharing paradigms, such as Dataspaces, promise to break down silos by providing a trusted, decentralized, and sovereign exchange of urban data across public and private stakeholders. Together, AI and Dataspaces hold the potential to significantly enhance the scalability, accuracy, and inclusiveness of UDTs.

This convergence opens a timely research frontier: how can UDTs, empowered by AI and Data Spaces, become sustainable and trustworthy instruments for smarter, more resilient, and human-centered cities?

This Special Issue aims to advance the scientific and practical understanding of how AI and Dataspaces can empower UDTs to address pressing urban challenges. It seeks contributions that combine theoretical insights, methodological advances, and empirical case studies to explore: (1) the design and governance of UDTs in the era of AI and federated data ecosystems; (2) the technical and semantic challenges of integrating geospatial data, real-time sensing, and citizen-generated information into trusted UDTs; and (3) the societal, ethical, and policy dimensions of data sharing and AI-driven decision-making in urban contexts.

The proposed Special Issue aligns with the scope of the International Journal of Geo-Information, which is dedicated to the theory, concepts, and applications of geographic information science. By focusing on UDTs as a foundation for building geospatial data infrastructures and decision-support systems, the Special Issue will contribute to the journal’s mission of advancing research on the acquisition, management, analysis, and visualisation of spatial information.

High-quality original research articles, technical notes, and comprehensive review papers are invited, addressing, but not limited to, the following themes:

Core Themes:

• AI for Urban Digital Twins: Machine Learning, Deep learning, and Generative AI for descriptive, predictive, diagnostic, and prescriptive analysis and what-if scenario simulation.
• Dataspaces for Urban Digital Twins: Architectures, standards, and governance models for interoperable, sovereign, and secure data exchange across cities.
• Geospatial Data Fusion and Interoperability: Technical and Semantic interoperability, ontologies, and linked data approaches to integrating heterogeneous urban datasets.
• Visualisation and Human–Computer Interaction: Innovative visualisation, immersive technologies, and participatory interfaces for UDT-enabled urban governance.
• Resilience and Sustainability Applications: UDTs for climate adaptation, disaster risk reduction, mobility optimisation, energy efficiency, and circular economy planning.
• Ethics, Trust, and Policy: Responsible AI, privacy-preserving data sharing, citizen empowerment, and governance frameworks for urban data ecosystems.

Types of Articles:

• Original Research Articles: Novel methods, models, and case studies.
• Review Articles: State-of-the-art surveys and systematic reviews of AI, UDTs, and Dataspaces.
• Technical Notes/Application Papers: Demonstrations of innovative tools, prototypes, or platforms in real-world urban contexts.
• Conceptual and Visionary Papers: Forward-looking perspectives on the future of UDTs in relation to AI and Dataspaces.

Prof. Dr. Dessislava Petrova-Antonova
Prof. Dr. Sisi Zlatanova
Dr. Mila Koeva
Guest Editors

Manuscript Submission Information

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