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WS3 Piloting Activities - Adding New AI-Based Capabilities to the LDT Toolbox

Brief Overview

This Work Strand is about experimenting with advanced AI-based services to push the boundaries of what Local Digital Twins (LDTs) can do. Pilots will test and apply cutting-edge approaches such as generative AI (GenAI4EU), virtual worlds, advanced simulation and modelling, and multi-sector services. These solutions will help cities improve critical services (energy, mobility, infrastructure, risk management) while involving citizens more actively in shaping their communities.

Key facts:

  • 3–4 pilots will be selected
  • Start of the pilots: May/November 2026
  • Duration: 18 months
  • Funding: 50% co-funding required
  • Second Round of open calls: February 2026
  • Third Round of open calls: May 2026

👉 Second Round of open calls now OPEN. Check it out here. Apply by 15/04/2026, 23:59 CET.

What is the goal?

  • Pilot AI-driven use cases that go beyond current LDT capabilities.
  • Test novel applications using generative AI, simulation, and immersive technologies.
  • Strengthen citizen participation through co-creation and democratic engagement.
  • Ensure that both advanced and less advanced communities can benefit from new AI tools.

Who Are We Looking For?

We invite applications from:

  • Municipalities, groups of municipalities, or regions, syndicates which already have a public service (whichever) and want to experiment with AI-enhanced LDT services to improve public services and resilience.
  • Cities, municipalities, or regions.
  • Technology innovators and solution providers in areas such as AI, generative AI, Citiverse/virtual worlds, simulation, and modelling, who are ready to collaborate with cities and transfer their solutions into real-world pilots.

Minimum consortium composition:

  • At least 2 public entities
  • Plus 1 other partner from the following:
    → Private entity (e.g., service provider)
    → Private association (legal status)
    → Trusted third party
    → Representative of a use-case sector

WS3 Requirements Cheat Sheet

This is a cheat sheet for Work Strand 3 (Adding New Advanced AI-Based Capabilities to the LDT Toolbox). For a complete list of requirements, please refer to the specific Call for Pilots Manual.

🎯 Minimum Conditions

  • 🏛️ 2+ local/regional public authorities from 2 different eligible countries
    → At least 1 must have a digitally mature LDT (Rq1)
    → Descriptive-level capabilities + dynamic data integration
  • 🤝 At least 1 additional partner (private entity, association, trusted third party, or sector representative)
  • 🔀 1+ cross-sectoral use case that is innovative and citizen-focused (Rq3)
    → Must include 2+ AI-based services
  • 🤖 Significant integration of AI into LDT services (Rq3)
    → AI may be used upstream (data cleaning) or downstream (analysis/decision support)

📋 Describe in the Application Form

  • Existing LDT/platforms + capabilities (Rq2)
    → Include URL/screenshots, architecture diagrams, data lifecycle
  • Current data governance for each pilot site (Rq4)
    → Target governance across political • technical • legal • organisational
  • Current use of AI/XR/edge computing in the public authorities (Rq32)
    → How, for what purpose, ethical/legal considerations
  • Shared local challenge addressed by AI-based service (Rq5)
    → Justify AI added value vs. non-AI solution
  • Alignment with EU priorities and LDT4SSC objectives (Rq6)
    → Green Deal, New European Bauhaus, LDT4SSC challenges
  • Project management and coordination (Rq7)
    → Teams, collaboration, recruitment, political endorsement
  • End-user engagement strategy (Rq8)
    → End-users must test the service before replication
  • Quadruple Helix stakeholders (Rq9)
    → 3 of 4 groups required (public • private • research • civil society)
  • Broader applicability for other EU communities (Rq10)
  • EU initiatives alignment (Rq11)
    → DSSC, Gaia-X, AIoD, EDIHs, TEFs (CitCom.AI), SIMPL, LDT Toolbox
  • Advanced Digital Technologies used in services (Rq20)
    → Intended use of AI (LLM fine-tuning, new model, integration...)
  • AI Act compliance for AI in public services (Rq21)
    → Describe ethical risk identification, assessment, mitigation
  • Contribution to governance, efficiency, innovation (Rq22)
  • Socio-economic and environmental effects + eco-design (Rq23)
  • Sustainability strategy post-pilot (Rq25)
    → Risks & mitigation (political, social, technical, operational, business, legal)
    → Plans for at least 1 year beyond project

🏗️ Build during the project

  • Each public authority must implement its own LDT instance (≥2 instances) (Rq13)
  • Each LDT must provide management access to all 7 LDT Layers (Rq13):
    1. Data Sources Layer
    2. Data Acquisition Layer
    3. Knowledge Layer (ML/AI models)
    4. Interoperability Layer
    5. Services Layer
    6. Orchestration Layer
    7. Visualisation Layer
  • Advanced capability: Predictive, Prospective, Prescriptive, or Diagnostic (Rq15)
    → LDT must be able to simulate scenarios
  • End-to-end traceability and proof of dependencies for all AI components (Rq33)
    → Track origin, usage conditions, governance of data and software dependencies

🧱 Provide as complementary material

  • 4 draft diagrams (Rq14):
    → Technical (deployment diagram, current + future)
    → Functional (activity diagram, current + future): end-to-end data and AI pipeline
    (collection → processing → training/validation/testing → deployment → monitoring)
  • Letter of Commitment with political endorsement
  • Ownership and Control Declaration (OCD)
  • Financial Form (.xlsx)
  • Ethics and Data Protection Self-Assessment
  • Contractual framework for LDT/service sustainability (Rq24)

🛠️ Engage during the project

Pilots are expected to engage with:

  • LDT4SSC methodology phases: Explore → Validate → Define → Implement (Rq12)
  • Semantic interoperability (MIM1) using open standards (Rq17)
    → e.g., NGSI-LD, LDES
  • Interoperability self-assessment: achieve score ≥3 by end (Rq18)
  • At least 5 foundational MIMs Plus (MIM0, MIM1, MIM2, MIM3, MIM6) (Rq19)
  • LDT4SSC Assets and Services Repository for asset sharing (Rq27)
  • LDT Toolbox Marketplace for code/models/algorithms (Rq27)
  • Open repositories for data models (Rq28)
  • Up-to-date documentation for LDT4SSC consortium (Rq31)

📦 LDT Toolbox Marketplace Requirements (Rq27)

Unless using the LDT AI Notebook, AI models must meet: - Deployable KServe package (Kubernetes via KServe, with InferenceService YAML) - Accessible model artefact location (resolvable storageUri) - Standard inference endpoint (V2-style API at /v2/models/{model_name}/infer) - Documented inputs/outputs and runtime (framework/runtime, version)

✏️ Specify upon application

  • Data, assets, services to be shared, sectors involved, providers and beneficiaries (Rq16)
  • MIMs Plus (MIM0–MIM8) the project will engage with (Rq19)
    → Current and planned compliance level (Initial, Partial, Full)
  • Main expected assets to be produced (Rq26)
    → See Annex 2.9 for list of potential assets
  • Deployment approach (hosted, SaaS, on-premise) (Rq14)
  • Replicability, Transferability, Scalability measures (Rq30)
    → Describe how assets can be transferred to at least one additional context
  • Equivalent open-source solution, if proprietary components used (Rq29)
  • Citiverse components/XR usage, if intended (Rq5)

⭐ Recommendations

  • Assess LORDIMAS maturity (Rc1)
    → Pilot Lead: Digitally Optimised
    → Others: ≥ Moderate
  • Include 3+ public authorities for better replicability (Rc2)
  • Pursue alignment with LDT Toolbox (Rc3)
    → Recommended tools:
    • EU LDT AI Notebook for algorithm/model creation
    • EU LDT Federated Learning for decentralised training
    • EU LDT Data Modeller for synthetic data generation
  • Consider federation with WS1 pilots using SIMPL GA Agent (Rc4)
  • Use open-source components and share enhancements (Rc5)
  • Include IP and exploitation rights in consortium agreement (Rc6)
  • Technically establish (Rc7):
    → DCAT data catalogue
    → Data management system (JSON-LD, RDF, NGSI-LD)
    → IAM (OAuth2, OpenID Connect, Verifiable Credentials)
    → ODRL-based data policy
  • Use MIT or Apache open licence (Rc8)
  • Record baseline data for Cost-Benefit Analysis (Rc9)
  • Assess eco-design maturity (General Policy Framework for Ecodesign) (Rc10)
    → At least 30 highest-priority criteria

💰 Financial Rules

  • Maximum grant per third-party: €500,000
  • Maximum cumulative grant per consortium: €1,000,000
  • Co-funding: 50% of total pilot costs from applicants' own resources
  • Indirect costs: 7% flat rate of direct costs

🧠 AI-Specific Highlights

Aspect Requirement
AI Integration Significant, either upstream (data) or downstream (analysis)
AI Services At least 2 AI-based services per use case
Added Value Must justify AI advantage over non-AI solution
Advanced Capability Predictive, Prospective, Prescriptive, or Diagnostic
Traceability End-to-end for all AI components (Rq33)
AI Act Full compliance required
Ethical Risks Must describe identification, assessment, mitigation
Toolbox Publishing KServe-deployable packages with documented APIs