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Digital Twins are used increasingly to support urban planning processes – by visualizing urban data, show-casing future scenarios and many other use cases. In general Digital twins are virtual representations of an object, process or system that can be used to run simulations to optimise efficiency and examine what-if scenarios. The technology has been primarily used for manufacturing to test products (e.g. as of 2018, GE had 1.2 million digital twins for 300,000 types of assets) but is quickly expanding to buildings, supply chains and entire cities as digital planning technology advances (Castro, 2019). Integrating data from the Internet of Things (IoT) with the advanced modelling capabilities of technologies such as geospatial information systems (GIS), virtual and augmented reality (VR/AR) and building information modelling (BIM) allows governments and industry to create predictions of how systems will react and respond to real-world data. Creating a feedback loop between the virtual and real worlds results in substantial improvements of processes and impacts, with time-saving and financial benefits.

The concept of digital twins is not new; for example, NASA has been running simulations of spacecraft for decades, but the rapid growth of connected sensors and endpoints with the rise of the IoT and advancements in artificial intelligence has opened up a myriad of possibilities for the planning and analysis tool. Potential uses for digital twins are still being imagined. Uses for cities currently include using digital twins to plan transportation systems, prepare for natural disasters and identify optimal locations to install solar panels. Future uses could include predicting how a disease will spread and informing optimal lockdowns and hospital reservations or using the tool to facilitate collaborations with other cities that have shared problems and mutual goals.

Problems to be solved:

Ease complex decision-makingEnable more efficient urban planningEnable more efficient project implementation and monitoringCreate better preparedness for emergency situationsMake urban data easier to understand


The main goal of Digital Twins is to ease urban planning processes, use data and increase transparency. Besides that, the solution achieves the benefits listed below. Whereas some benefits are likely to be fulfiled with a basic implementation of the solution, the fulfilment of the potential benefits depends on the functions implemented in a specific project.

Main benefits
  • Enhanced data analysis

  • Facilitating citizen engagement

  • Improving personnel efficiency

  • Improving life quality

  • Enhanced data collection

Potential benefits
  • Increased data transparency

  • Enhanced data collection

  • Efficient integration of renewables


Functions help you to understand what the products can do for you and which ones will help you achieve your goals.
Each solution has at least one mandatory function, which is needed to achieve the basic purpose of the solution, and several additional functions, which are features that can be added to provide additional benefits.

Mandatory functions
    Connect with data about the city

    accessing data (e.g. from sensors) about the different components of a city

    Simulate processes

    use real time data to create simulations that can predict how a product or process will perform

    Provide information to optimise processes

    provide decision makers with information on how to optimise simulated products or processes

Potential functions
    Prepare for emergency situations

    Simulate disaster response to best prepare

    Communicate to stakeholders

    features enabling citizen participation and stakeholder collaboration

Products offering these functions

Digital Twin for PEDS

This virtual twin focuses on all energy demands (electricity, district heating) and on-site electricity production from PV.

CityGML Based District Energy Simulation

Combining geographical and building information with energy simulation, allows for quantifying energy related as well as potentially other indicators for city districts.

City 3D-model - Supporting Sustainable City Development

Integration of Espoo’s 3D City model in the planning process of positive energy districts (PEDs)


Digital twins cover a broad range of applications and well-established categories may not encapsulate all aspects of each adoption of the technology. The following categories were identified by the High Value Manufacturing Catapult Visualisation and VR Forum (2018).


The simplest form of digital twin, this variant displays the live state of a physical asset or process to human observers. The value provided is the ability for a city to have people act on the information provided. For example, a city could have a digital twin that tracks the condition of streets and roads, letting observers know when the conditions require maintenance to be done.


Interactive digital twins take control of at least one aspect of the physical asset or process to achieve better performance, either from internal monitoring or more complex analysis. For example, thermostats in public housing units report when the temperature has gone above or below a certain threshold, triggering automatic responses to adjust the amount of heat being produced by the heating system.


This is the most complex type of digital twin. Predictive digital twins monitor state information over time to provide augmented information such as recommendations and/or warnings to human observers and/or other digital systems. For example, to expand the street monitoring system example from the Supervisory Variant, the system could predict when streets will require maintenance based on the amount of traffic the streets encounter. The difference here is that in the Supervisory example, the city needs to rely on their own interpretation of the street conditions whereas in the Predictive case, the city is directly informed when the conditions will require maintenance based on information that was not available in the Supervisory case. This reduces the amount of human judgment required.

City Context

Digital twins are supported by cities with:

  • IoT sensors embedded in the city’s core services
  • High levels of connectivity and useable data
  • GIS system in place

Supporting Factors

Digital Twins of smart cities is a new application of several technologies that need to be interoperable and high-performance. A capable and innovative team, with in-depth knowledge of how these technologies and their data function, will support a city in getting the most out of their digital twin. A brief overview of some of these technologies that are integrated into digital twins follows:

  • Geospatial Information Systems: The core spatial modelling technology of a city’s digital twin, GIS connects different kinds of geospatial data to create a single view and provide advanced analytics of the system.
  • Building Information Modelling: When integrated with GIS, BIM provides a rich dataset for the built environment to create more accurate models. Real-time data requires interoperability with the Internet of Things.
  • Augmented or Virtual Reality: Enhances real-life perception of digital twins. This can be particularly useful in collaborative and participatory processes in urban planning.

Stakeholder Mapping

Stakeholder Map of an Urban Digital Twin (BABLE, 2021)

Market Potential

Across all industries, the global digital twin market size was valued at USD 3.1 billion in 2020 with an expected CAGR of 58% until 2026, when it is projected to reach USD 48.2 billion. The response to the COVID-19 pandemic is expected to be a major driver of the digital twin market growth, in particular in the healthcare and pharmaceutical industries, as well as for cities to better monitor outbreaks and respond to the changes in daily life brought by the pandemic (Markets&Markets, 2020).  

Cost Structure

Digital Twins on the city level are still a nascent technology and the cost structures will vary widely based on how advanced the software and hardware the city decides to invest in. One example, Virtual Singapore, had a budget of $73 million for developing their digital twin platform and for researching the tools and technologies required (NRF, 2020).

The creation of this solution has been supported by EU funding

Use Cases



Photorealistic Digital Twin to visualise urban development

The aim was to visualise a future school extension in the most immersive and photorealistic way to share it with the local community.


TWIN4ROAD@Essen: AI-based analysis and forecast

In the coming years, the city of Essen will rely on artificial intelligence (AI) for the damage assessment of roads. Named TWIN4ROAD, the Office for Geoinformation, Surveying and Cadastre together with Point Cloud Technology, HPI Potsdam and Straßen.NRW launched a three-year research project.




Increasing Energy Efficiency in Buildings Using AI

TPC identified areas of improvement for the management of the HVAC systems of two buildings. We performed a building energy performance analysis using artificial intelligence techniques to make an accurate prediction of the energy needs of these buildings to reduce their energy inefficiencies.







BIMROCKET is an open source platform for managing Building Information Modeling (BIM) projects, a collaborative working methodology for the construction industry. It allows viewing and editing building models and storing BIM projects in an OrientDB database.

Social Responsibility



Communicate Urban Real Estate Development Projects With the Help of Digital Twins

Fällanden did a municipality development project and wanted to offer their population a realistic, interactive visualisation with which the context of the newly planned buildings could be clearly shown. With the resulting clarity, a more participatory and faster decision-making process was achieved.



Digital Twin for Hovinbyen

Providing a co-creation workspace for a wide range of stakeholders for a large city development project, to analyse the impact of the development through simulation and analysis.

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