Digital Twins for sustainable cities and environments

Digital Twins (DTs) are rapidly becoming one of the most exciting innovations in smart cities, bringing together 3D modelling, geospatial data, IoT sensors, and AI to create a live, dynamic digital replica of the physical world. Unlike traditional 2D/3D maps, a Digital Twin can display the past, present, and predicted future states of buildings, infrastructure, and urban processes, enabling stakeholders to test ideas, identify problems early, and make informed decisions. Today, many municipalities struggle with fragmented data, poor interoperability, and outdated tools that cannot support continuous monitoring or data-driven planning.

This research topic invites students to explore innovative Digital Twin-based solutions by combining geospatial, remote sensing and domain-specific data with AI and decision-support methods to predict future scenarios (e.g., heat stress, land-use change, mobility flows).

There are several possible directions:

A case study in the Netherlands. The Municipalities are interested in digital twinning to address topics related to urban heat stress, sustainable urban planning, energy, air or water monitoring. One example is the Municipality of Hardenberg, which is interested in simulating where the hot and cold stress areas are in the cities and how our predictions and simulations can reach/inform vulnerable people (children and old people). Depending on the availability of data and students' interest, the topic will be further elaborated.
Digital Twin for cities/regions out of the Netherlands, e.g. Shanghai, Johannesburg, Wuppertal or your city (simulations for smart city planning to be discussed), Jordan (on water management). (Watch for inspiration, our previous work on the videos shared at the bottom of this page)

Topic objectives and methodology

The objective of this research is to design and prototype a Digital Twin (DT) framework that enables integrated, data-driven urban analysis by combining geospatial, remote sensing, IoT, and domain-specific datasets with AI-based modelling and decision-support tools.

The methodology may include collecting and preprocessing multi-source urban data, harmonising it through semantic models and interoperability standards, building a unified 2D/3D environment for visualisation and interaction, and applying machine learning or predictive analytics to simulate future scenarios such as heat stress, mobility patterns, or others. The workflow can also incorporate stakeholder needs through interviews or co-design sessions, ensuring the resulting DT tools effectively support municipal planners in evidence-based decision-making.

References for further reading

Papers:

Iván Cardenas-Leon, Pirouz Nourian, Mila Koeva, Karin Pfeffer, Cross-sectoral digital planning Drinking Water-Urban Heat-Housing Provision indicators dataset,
Data in Brief, Volume 64, 2026, 112310, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2025.112310.
Cárdenas, I., Koeva, M., Davey, C. & Nourian, P., (2024). Urban digital twin-based solution using geospatial information for solid waste management, Sustainable Cities and society
Campoverde C, Koeva M, Persello C, Maslov K, Jiao W, Petrova-Antonova D. Automatic Building Roof Plane Extraction in Urban Environments for 3D City Modelling Using Remote Sensing Data. Remote Sensing. (2024); 16(8):1386. https://doi.org/10.3390/rs16081386
Cárdenas, I., Koeva, M., Davey, C. & Nourian, P., (2024). Solid Waste in the Virtual World: A Digital Twinning Approach for Waste Collection Planning. Springer, p. 61-74 (Lecture Notes in Geoinformation and Cartography; vol. XIII).
La Guardia, M. , & Koeva, M. N. (2023). Towards Digital Twinning on the Web: Heterogeneous 3D Data Fusion Based on Open-Source Structure. Remote sensing, 15(3), Article 721. Advance online publication. https://doi.org/10.3390/rs15030721
de Vries, J., Atun, F., & Koeva, M. N. (2023). Analysis of Potential Disruptions from Earthquakes in Istanbul and 3D Model Based Risk Communication. IDRiM Journal, 13(2), 60-89.
Kumalasari, D. , Koeva, M. , Vahdatikhaki, F., Petrova Antonova, D. , & Kuffer, M. (2023). Planning walkable cities: Generative design approach towards digital twin implementation. Remote sensing, 15(4), Article 1088. https://doi.org/10.3390/rs15041088
Cárdenas, I. L., Morales, R., Koeva, M., Atun, F., & Pfeffer, K. (2023, Aug 31). Digital Twins for Physiological Equivalent Temperature Calculation Guide. Zenodo. https://doi.org/10.5281/ZENODO.8306456
Hu, C., Fan, W., Zeng, E., Hang, Z., Wang, F., Qi, L., & Bhuiyan, M. Z. A. (2021). Digital twin-assisted real-time traffic data prediction method for 5G-enabled internet of vehicles. IEEE Transactions on Industrial Informatics, 18(4), 2811-2819.
Z. Wang et al., "Mobility Digital Twin: Concept, Architecture, Case Study, and Future Challenges," in IEEE Internet of Things Journal, vol. 9, no. 18, pp. 17452-17467, 15 Sept.15, 2022, doi: 10.1109/JIOT.2022.3156028.
La Guardia, M., Koeva, M., D'Ippolito, F., & Karam, S. (2022). 3D DATA INTEGRATION FOR WEB BASED OPEN SOURCE WebGL INTERACTIVE VISUALISATION. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences.
Lehtola, V. V., Koeva, M., Elberink, S. O., Raposo, P., Virtanen, J. P., Vahdatikhaki, F., & Borsci, S. (2022). Digital twin of a city: Review of technology serving city needs. International Journal of Applied Earth Observation and Geoinformation, 102915.
Khawte, S. S., Koeva, M. N., Gevaert, C. M., Oude Elberink, S., & Pedro, A. A. (2022). DIGITAL TWIN CREATION FOR SLUMS IN BRAZIL BASED ON UAV DATA. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences.
Ying, Y., Koeva, M., Kuffer, M., & Zevenbergen, J. (2022). Toward 3D Property Valuation—A Review of Urban 3D Modelling Methods for Digital Twin Creation. ISPRS International Journal of Geo-Information, 12(1), 2.
de Vries, J., Atun, F., & Koeva, M. N. (2022). ASSESSING POTENTIAL DISRUPTIONS FROM EARTHQUAKES IN THE HISTORICAL PENINSULA IN ISTANBUL USING 3D MODELLING. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences.
Grift, J., Persello, C., Koeva ,M., (2024). CadastreVision: A benchmark dataset for cadastral boundary delineation from multi-resolution earth observation images. ISPRS Journal of Photogrammetry and Remote Sensing, Volume 217, Pages 91-100, https://doi.org/10.1016/j.isprsjprs.2024.08.005.
Metaferia, M. T., Bennett, R. M., Alemie, B. K., & Koeva, M. (2023). Furthering Automatic Feature Extraction for Fit-for-Purpose Cadastral Updating: Cases from Peri-Urban Addis Ababa, Ethiopia. Remote Sensing, 15(17), 4155.
Ahsan, M. S., Hussain, E., Ali, Z., Zevenbergen, J., Atif, S., Koeva, M., & Waheed, A. (2023). Assessing the Status and Challenges of Urban Land Administration Systems Using Framework for Effective Land Administration (FELA): A Case Study in Pakistan. Land, 12(8), 1560.

Project outputs: https://sites.google.com/view/milakoeva/projects

Recent recordings:

Mila Koeva: 🚀ASTRAIOS Webinar #10: Earth Observation and Geospatial Data for Digital Twins Global Perspectives..

Digital Twins - projects with students: Session1:https://lnkd.in/eRBkyfTt

Digital Twins - projects with students: Session2: https://lnkd.in/ezefMFdc

Talks: https://www.utwente.nl/en/digital-society/research/digitalisation/digital-twin-geohub/talks/

Research: https://www.utwente.nl/en/digital-society/research/digitalisation/digital-twin-geohub/research/msc-thesis/

How can topic be adapted to Spatial Engineering

The topic is very suitable for Spatial Engineering students since it is multidisciplinary and is focused on wicked problems.