Use Cases
Valencia
Services:
external: deliver service to citizens
internal: help decision making process
Sofia, Bulgaria
LDT Purpose:
Support data-driven decision making through implementation of several scenarious related to urban planning, air pollution, climate change (e.g. urban heat island and heat risk), walkability, pedestrian wind comfort, etc.
City-wide/application-specific:
City-wide
Chosen Category:
Predictive LDT: It has capabilities to anticipate events such as traffic congestion or flooding and allows to analyse their impacts. It requires near-real-time data flows and vetted models, wchih may include AI. This type of LDT allows for the possibility of virtualizing disaster exercises using replay of historic data. It will require some expertisse such as domain experts and data engineers to be available and needs a mature-data organisation.
List of capabilities:
Data Platform that integrates different data sources
Real-time data sources
Models / Simulations (ML, AI)
Visualisation: 3D
Interoperability Issue:
Lack of data models and data standards at data providers side, which requires additional effort for data management. Data silos and missing availability of APIs for data exchange at municipal level (legacy systems and siloed infrastructures in the municipality are often not designed to interoperate). Different standards used by distinct communities in principal. For instance, the FIWARE community employs Smart Data Models, while the geospatial community relies on OGC standards like CityGML. Even small terminological differences (e.g., “roadwork” vs. “street maintenance”) can lead to integration failures or misinterpretation unless a common vocabulary or semantic mediator is applied. This disparity requires mapping these standards to ensure seamless data interoperability. AI models require high-quality, semantically rich, and consistent datasets. Integrating fragmented, incomplete, or inconsistent municipal data degrades AI performance and trustworthiness. Beyond technical aspects, challenges also arise in organisational and cultural domains, such as establishing common rules, governance frameworks, and fostering a culture of data sharing and innovation within public administrations. Deployment and maintaining interoperable systems — especially involving AI, semantic models, or real-time data — requires skilled personnel, often missing in public administration
Brussels, Belgium
LDT Purpose:
Anything, with a current focus on mobility
City-wide/application-specific:
Both
Chosen Category:
Experimental LDT: It allows to run what-if simulations and scenario management, therefore it requires more structured data. Resulting models will have to be configured and deployed once and will allow for sufficient verification of the validity of their outcomes. This type of LDT requires a limited involvement of data and domain experts.
List of capabilities:
Data Platform that integrates different data sources
Real-time data sources
Models / Simulations (ML, AI)
Visualisation: 3D
Interoperability Issue:
Legal aspects in contracts for data sharing agreement
Madrid, Spain
While the use cases are in Madrid, the existing solution may cover any type of community
LDT Purpose:
Prediction of energy consumption in buildings and security monitoring in roads maintenance, prevention of computing networks attacks
City-wide/application-specific:
Both
Chosen Category:
Predictive LDT: It has capabilities to anticipate events such as traffic congestion or flooding and allows to analyse their impacts. It requires near-real-time data flows and vetted models, wchih may include AI. This type of LDT allows for the possibility of virtualizing disaster exercises using replay of historic data. It will require some expertisse such as domain experts and data engineers to be available and needs a mature-data organisation.
List of capabilities:
Real-time data sources
Models / Simulations (ML, AI)
Visualisation: 3D
Interoperability Issue:
common criteria to assess the required level of data quality
Leuven, Belgium
LDT Purpose:
Different: 1) Mobility, 2) Nature Based Solutions implementation (climate adaptive measures), 3) Cultural (to be)
City-wide/application-specific:
Application-specifc
Chosen Category:
Awareness LDT: It consists of an explorative visualisation with some layers, it can include sensors and sensor data. This type of LDT has limited data management capabilities and, accordingly, does not require much user expertise to operate it. It can be used for situational awareness in terms of traffic, logistics, environmental, demographic, health, etc. It is typically the start of the digital twin roadmap.
List of capabilities:
Data Platform that integrates different data sources
Real-time data sources
Models / Simulations (ML, AI)
Citizen Participation
Visualisation: 3D
Visualisation: Dashboards
Interoperability Issue:
different datasets needed in different models are not interoperable
Espo, South Finland
LDT Purpose:
Additional tool for urban design and development
City-wide/application-specific:
For Built environment in Espoo
Chosen Category:
Experimental LDT: It allows to run what-if simulations and scenario management, therefore it requires more strucutred data. Resulting models will have to be configured and deployed once and will allow for sufficient verification of the validity of their outcomes. This type of LDT requires a limited involvement of data and domain experts.
List of capabilities:
Data Platform that integrates different data sources
Real-time data sources
Models / Simulations (ML, AI)
Citizen Participation
Visualisation: 3D
Visualisation: Interactive
Interoperability Issue:
Exploring interoperability within the city but also with external stakeholders
Cartagena, Spain
LDT Purpose:
Mobility, Low Emission Zones
City-wide/application-specific:
Chosen Category:
Predictive LDT: It has capabilities to anticipate events such as traffic congestion or flooding and allows to analyse their impacts. It requires near-real-time data flows and vetted models, wchih may include AI. This type of LDT allows for the possibility of virtualizing disaster exercises using replay of historic data. It will require some expertisse such as domain experts and data engineers to be available and needs a mature-data organisation.
List of capabilities:
Data Platform that integrates different data sources
Real-time data sources
Models / Simulations (ML, AI)
Citizen Participation
Visualisation: 3D
Visualisation: Interactive
Visualisation: Dashboards
Interoperability Issue:
The CHIMERE air quality model, used to simulate and predict the atmospheric dispersion of pollutants based on emissions, weather, and topography.
Netherlands
LDT Purpose:
The purpose of the Local Digital Twin (LDT) for several cities in the Netherlands is to provide a dynamic, data-driven platform that supports local and regional governments in making more informed, transparent, and sustainable decisions. By integrating geospatial data, real-time sensor inputs, and artificial intelligence, the LDT enables stakeholders to visualize, simulate, and assess the impact of various policy scenarios related to urban planning, energy transition, urban heat island, mobility, and climate adaptation. Designed to be modular, interoperable, and scalable, the LDT facilitates collaboration across municipalities, provinces, academia, and industry, while promoting citizen engagement and aligning local actions with national and European sustainability goals.
City-wide/application-specific:
Application-specific
Chosen Category:
Predictive LDT: It has capabilities to anticipate events such as traffic congestion or flooding and allows to analyse their impacts. It requires near-real-time data flows and vetted models, wchih may include AI. This type of LDT allows for the possibility of virtualizing disaster exercises using replay of historic data. It will require some expertise such as domain experts and data engineers to be available and needs a mature-data organisation.
List of capabilities:
Data Platform that integrates different data sources
Real-time data sources
Models / Simulations (ML, AI)
Visualisation: 3D
Visualisation: Dashboards
Interoperability Issue:
In implementing the Local Digital Twin (LDT) in the Netherlands, we have faced several interoperability challenges, including inconsistent data across municipalities and sectors, which complicate the integration of spatial and administrative datasets. Legacy systems often lack modern APIs or compatibility with open standards, making system integration difficult, while fragmented governance and varying data access policies further hinder seamless collaboration and data sharing. Aligning dynamic real-time data (e.g., traffic, energy use) with static datasets (e.g., zoning plans) also poses technical challenges for ensuring coherent simulations. Moreover, deploying and maintaining interoperable systems—especially those involving AI, semantic models, or real-time sensor data—requires skilled personnel, who are often lacking in public administrations, creating capacity gaps that limit sustainable implementation and scaling. A further challenge is the strong dependency of municipalities on different commercial digital twin solutions, each with proprietary data models and interfaces, which restricts flexibility, interoperability, and knowledge ownership. These issues underscore the need for standardized frameworks, cross-sectoral coordination, and long-term investment in both digital and human infrastructure.
Case Study Presentation:
what is the main aim/purpose of the LDT?
section on: orchestration is re data (camera, sensors etc) to model -> which data goes in adn what data goes out?
dependency on commercial data providers/ information models- we need to find an information model to connect with the base LDTs (eg.: 3D model) to be able to add data models. -— orchestration of data (knowledge graphs etc).-— extensions for application areas.
keep the categories of use cases
type of standards used for LDT purposes to understand the scale of interoperability
governance: authenticator services (access, rights, purposes), participants
LDT allows for external participation and if this is made possible, evolution goals -> does it want to become a citiverse
ask for the step by step of the LDT - re layers.
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