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IJGI
Special Issues
Urban Digital Twins Empowered by AI and Dataspaces
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Urban Digital Twins Empowered by AI and Dataspaces

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: 23 December 2026 | Viewed by 2729

Special Issue Editors

Prof. Dr. Dessislava Petrova-Antonova

E-Mail Website
Guest Editor
1. Department of Software Engineering, Sofia University “St. Kliment Ohridski”, 1113 Sofia, Bulgaria
2. GATE Institute, Sofia University “St. Kliment Ohridski”, 1113 Sofia, Bulgaria
Interests: urban digital twins; data interoperability and semantic enrichment; data-intensive systems; generative AI for urban planning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sisi Zlatanova

E-Mail Website
Guest Editor
Faculty of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia
Interests: 3D indoor modelling; 3D GIS; integration of BIM and GIS; 3D spatial analysis; DBMS; emergency response
Special Issues, Collections and Topics in MDPI journals
Dr. Mila Koeva

E-Mail Website
Guest Editor
Department of Urban and Regional Planning and Geo-Information Management, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
Interests: 3D modelling and digital twins (SPSS/SDSS, LDT, gaming); resource management and decision-making for cadastral and urban planning applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • urban digital twin
  • artificial intelligence
  • human–computer interaction
  • generative AI
  • urban resilience and sustainability
  • geospatial data fusion and interoperability
  • urban ethics, trust, and policy

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Published Papers (2 papers)

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21 pages, 7166 KB  
Article
Geometric Reliability of AI-Enhanced Super-Resolution in Video-Based 3D Spatial Modeling
by Marwa Mohammed Bori, Zahraa Ezzulddin Hussein, Zainab N. Jasim and Bashar Alsadik
ISPRS Int. J. Geo-Inf. 2026, 15(3), 125; https://doi.org/10.3390/ijgi15030125 - 13 Mar 2026
Viewed by 505
Abstract
Video-based photogrammetric reconstruction is increasingly used when high-resolution still images are unavailable. However, limited spatial resolution, compression artifacts, and motion blur often reduce geometric accuracy. Recent advances in learning-based image super-resolution (SR) offer a promising preprocessing method, but their geometric reliability within photogrammetric [...] Read more.
Video-based photogrammetric reconstruction is increasingly used when high-resolution still images are unavailable. However, limited spatial resolution, compression artifacts, and motion blur often reduce geometric accuracy. Recent advances in learning-based image super-resolution (SR) offer a promising preprocessing method, but their geometric reliability within photogrammetric workflows remains not well understood. This study provides a controlled quantitative evaluation of learning-based super-resolution for video-based 3D reconstruction. Low-resolution video frames are enhanced using two representative methods: an open-source real-world SR model (Real-ESRGAN ×4) and a commercial solution (Topaz Video AI ×4). All datasets are processed with the same Structure-from-Motion and Multi-View Stereo pipelines and tested against terrestrial laser scanning (TLS) reference data. Results show that super-resolution significantly increases reconstruction density and improves the recovery of fine-scale surface details, while also leading to greater local surface variability compared with reconstructions from the original video; photogrammetric stability remains consistent despite these changes. The findings highlight a fundamental trade-off between reconstruction completeness and local geometric accuracy and clarify when enhanced video imagery via super-resolution can be a reliable source for 3D reconstruction. These results are especially important for spatial data science workflows and AI-powered 3D modeling and digital twin applications. Full article
(This article belongs to the Special Issue Urban Digital Twins Empowered by AI and Dataspaces)
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22 pages, 5177 KB  
Article
VGGT-Geo: Probabilistic Geometric Fusion of Visual Geometry Grounded Transformer Priors for Robust Dense Indoor SLAM
by Kai Qin, Jing Li, Sisi Zlatanova, Haitao Wu, Hao Wu, Yin Gao, Dingjie Zhou, Yuchen Li, Sizhe Shen, Xiangjun Qu, Zhenxin Zhang, Banghui Yang and Shicheng Xu
ISPRS Int. J. Geo-Inf. 2026, 15(2), 85; https://doi.org/10.3390/ijgi15020085 - 16 Feb 2026
Viewed by 1489
Abstract
With the rapid evolution of Digital Twins and Embodied AI, achieving fast, dense, and high-precision 3D perception in unknown environments has become paramount. However, existing Visual SLAM paradigms face a critical dilemma: geometry-based methods often fail in texture-less areas due to feature scarcity, [...] Read more.
With the rapid evolution of Digital Twins and Embodied AI, achieving fast, dense, and high-precision 3D perception in unknown environments has become paramount. However, existing Visual SLAM paradigms face a critical dilemma: geometry-based methods often fail in texture-less areas due to feature scarcity, while learning-based approaches frequently suffer from scale drift and unphysical deformations. To bridge this gap, we propose VGGT-Geo, a novel SLAM system that synergizes generative priors from Large Foundation Models with multi-modal geometric optimization. Distinguishing itself from simple cascaded architectures, we construct a Probabilistic Geometric Fusion framework, consisting of (1) Generative Warm-start, leveraging the holistic scene understanding capabilities of the VGGT, (2) Confidence-Aware Optimization to extract dense features via DINOv3 and predict their confidence map, and (3) a Multi-Modal Constraint Closure that fuses point-line features and metric depth priors to constrain rotational Degrees of Freedom in Manhattan Worlds. We conducted systematic evaluations on TUM, Replica, Tanks and Temples, and a challenging self-collected dataset featuring extreme lighting and texture-less walls. Experimental results demonstrate that VGGT-Geo exhibits superior robustness and accuracy in unseen environments. On our most challenging dataset, it achieves an Absolute Trajectory Error of 4–5 cm and a Relative Rotation Error of 0.79°, outperforming current state-of-the-art methods by approximately 50% in trajectory accuracy. This study validates that synergizing the intuition of Large Foundation Models with geometric rigor is a viable path toward next-generation robust SLAM. Full article
(This article belongs to the Special Issue Urban Digital Twins Empowered by AI and Dataspaces)
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