Aims & Scope

Digital twin (DT) is an integrated multi-physics, multi-scale, and probabilistic virtual representation of an object, system, or activity that mirrors the evolution of its physical counterpart over time. In smart cities and the built environment, DTs connect urban-scale systems (e.g., mobility, energy, water, environment, and public safety) with asset- and project-scale entities (e.g., buildings and infrastructure) by integrating semantic and geometric models with streaming data to support real-time sensing, predictive analytics, dynamic control, and information sharing. A range of enabling technologies, such as Internet of Things (IoT), computer vision, cloud/edge computing, blockchain, and AI, are increasingly adopted to collect data, ensure trustworthy provenance, and facilitate decision-making across the full life cycle from planning and design to construction, operation, maintenance, and city management. Real-time data and model updates can be shared in cloud space to improve cyber-physical visibility and traceability for multi-stakeholder collaboration. By fusing multi-dimensional information (e.g., status, location, performance, quality, cost, carbon, and safety), high-fidelity DTs can provide timely situational awareness and decision support for both city operators and construction practitioners, enabling safer workflows and more efficient handling with improved accuracy, timeliness, and transparency. To make the latest research presented at the 6th Digital Twin International Conference 2026 (DigiTwin2026) widely available, DigiTwin is extending a Call for Papers for a Special Section reflecting the scope of the conference.