Towards networked Local Digital Twins in the EU

Expected outcomes and deliverables of these three working strands are:

• An EU ecosystem of LDTs sharing a common blueprint.
• An uptake in the number of local platforms and digital twins, with associated services compatible with the EU data cloud infrastructure, reusing interoperable and open-source tools from the LDT toolbox.
• New AI-based services extending and supplementing the catalogue of the EU LDT toolbox for cities and communities.
• Increasing and maturing the AI services offering the public domain with new predictive and modelling services and immersive solutions, paving the way to the CitiVerse.

Building on the Data Space for Smart Communities and the EU Local Digital Twin (LDT) Toolbox, both supported by the WP 2021-2022, and in synergy with the action “Developing CitiVerse” (see 2.3.2), the main objective of this action is to consolidate existing results and move towards an ecosystem of mature and networked LDTs across the EU to help cities and communities achieve economies of scale when developing their data platform and LDT services.

EU cities and communities are evolving towards digital communities at a different pace and this topic is proposing three complementary activities to help them achieve economies of scale when adopting data, LDT and AI-based solutions. Previous WP supported the creation of an EU data space for smart communities (and its validation via pilots) and action 2.3.2 pioneers innovative services using ‘Citiverse’ technology. Under the WP 2021-22, the Commission has also launched procurement actions to support the development of a LDT toolbox in the EU. However, more effort is needed to create a pan-European ecosystem of digital twins that can connect and scale up towards a future “EU CitiVerse”. To achieve the approach is to support the development of open source LDT solutions based on common needs of EU cities and communities (both urban and rural), to connect them and to enrich them with additional innovative services. In order to address the different maturity level of cities and communities, this action will help (1) connect and further develop the already existing LDTs, (2) gain a critical mass of smart communities’ data sets, as well as (3) deploy, test, and add more complex AI-based elements to the LDT toolbox, addressing the needs of digitally advanced smart communities.

These and future actions regarding LDTs are contributing to the effective implementation of a Multi Country Project (MCP) in the area of Connected Public Administration.

The action is divided in three objectives, each to be achieved through a separate work strand:

• Objective 1: Technical inter-connection of existing LDTs: Connecting data platforms and LDTs from cities and communities that already have a LDT in place, in order to create an EU “federation” of LDTs. Reinforced interoperability through the aggregation of LDTs at a larger scale (cross sectors, cross cities, and cross borders) will help to scale up the EU common data sets and open-source solutions. It will also facilitate the way for less advanced cities and communities who wish to join the existing EU LDT ecosystem.
• Objective 2: Creation of LDTs based on common needs: Developing open-source pilots of LDTs services, based on shared needs of cities and communities that already have a local data platform and/or a LDT and want to expand them with new real-life use case services. These services should aim to improve decision-making processes and citizen interaction; reduce risks, costs and downtime; enhance resilience and sustainability of LDT platforms and enable new value creation.
• Objective 3: Adding new complex AI-based tools to the LDTs toolbox: Complementing the EU LDT toolbox launched under WP2021-22 with additional complex AI-based and innovative services (e.g., for adaptable multi-sector considerations, advanced simulation and modelling approaches including bottom-up self-organised models). The AI services should be developed and tested within existing cities/communities and be transferable to other contexts.

These objectives will be implemented through three work strands by a single project that will provide cascading funding to the third parties through a single call.

The selected proposal will manage a community of projects selected by cascading funding mechanism. The work of these projects will be implemented through three main work strands:

• Work strand 1: Connect existing LDTs at EU level and lay down the foundations of an EU LDT ecosystem. When connecting LDTs, and their related data sets, projects should align with the smart cloud-to-edge middleware platform Simpl to achieve interoperability and make use of horizontal services available under the Digital Europe programme such as the Smart Communities dataspace blueprint, the EU data cloud infrastructure and the EU LDT toolbox.
• Work strand 2: Launch new pilot LDT services based on common needs of cities and communities to enhance interoperability and collaboration. The services should be tested in real-life conditions and expand the EU LDT ecosystem by making use of and contributing to the development of the EU LDT toolbox catalogue. The pilots should include at least the following activities:

1. designing real life use cases based on open and interoperable data sets across sectors. The use of these data sets should follow requirements of the data space for smart cities and communities' blueprint;
2. building and deploying the pilot services on LDT platforms at city/community level with real use case data;
3. defining roadmaps for making available the LDT-based services on the EU LDT toolbox catalogue

• Work strand 3: Add new complex AI-based components to the forthcoming EU LDT toolbox to address the needs of cities/communities:

1. Develop and deliver new AI-based services to create simulations, predictive models and forecast in a variety of sectors and use cases amongst which the New European Bauhaus, public services and communities’ resilience;
2. Develop and deliver solutions for designing, optimising, and testing urban policies in dynamic complex environments with several interwoven sectors (e.g., traffic volume and pollution levels). AI classical optimization may be complemented with self-organised bottom-up solutions that will offer the necessary adaptability and robustness;
3. Develop and deliver new complex AI-based services related to citizen’s engagement, with or without the use of eXtended Reality technology to address aspects of the New European Bauhaus initiative and implement a sustainable, democratic and citizen-centric CitiVerse.

To maximize the scale-up and reusability of the new services, these should be designed in such a way that they can be adopted by different maturity levels LDT architectures.