Conference Agenda

Digital twins that serve as virtual representations of real-world objects and structures, are used in various applications for urban environments. Challenges for creating and maintaining digital twins involve data acquisition, fusion of heterogeneous data types, AI-based data analysis, and the integration into existing applications and workflows. In this paper, we present the concept and implementation of a dynamic digital twin from a city’s perspective. The concept avoids explicit modeling to simplify the creation of a comprehensive data basis that can be easily updated frequently. 3D point clouds with semantics are used as representations for static objects and structures, such as buildings, infrastructure and subsurface structures. Dynamic aspects are represented through a time series of sensor data to enable real-time monitoring and change detection applications. A centralized data repository for applications such as infrastructure monitoring, condition assessment, and inventory management represent a basis to support decisions. We present typical use cases and challenges from the perspective of a city and how the dynamic digital twins can create significant added value.

Digital twins have gained increasing attention, regarded as a tool to facilitate decision-making in the cities. However, the current discourse predominantly focuses on technical aspects while overlooking the human aspect in urban digital twins. This work is intended to propose a conceptual framework that addresses the role of humans in relation to the urban environment, therefore highlighting the social value of urban digital twins. The proposed framework is subsequently implemented in a specific case study, validating its feasibility in practice. By incorporating human sensing data, such as participatory data, urban digital twins have the potential to represent the dynamic interaction between people and environments, generating a holistic physical-social-virtual system.

Urban Digital Twins (UDTs) have emerged as essential tools for managing city operations, forming the basis of smart city solutions. They offer a digital representation of the physical urban environment, which supports various city applications such as monitoring mobility, air quality, and modelling simulations. To accurately represent the physical world, UDTs need to be updated continuously to reflect the changes in the urban environment on time. The Internet of Things (IoT) enables real-time data collection to capture these changes. Combined with 3D city models, IoT allows the interactive visualisation of patterns and trends in UDTs. In this study, we conduct investigations on the requirements for the web visualisation of semantic 3D city models enriched with time-dependent properties from IoT and simulation data. We explore the 3D models and IoT data integration requirements, 4D web visualisation design considerations, and the technical implementation requirements for rendering dynamic properties for UDTs applications. The results from our initial experiments form the basis for the next steps towards creating compelling 4D visualisations catering to different user and application needs.

Rapid urbanisation and limited land availability have led to increased consideration of underground spaces. This increased utilisation of subterranean space has created a rise in its value, highlighting the significance of ownership of underground areas. A fully-integrated digital model that effectively represents underground space ownership is vital for communicating rights, restrictions, and responsibilities (RRRs) in underground spaces. Underground assets are often built and developed vertically at different time slots, resulting in changes to the legal ownership of underground spaces. Therefore, underground RRRs change over time due to factors such as new subdivisions, consolidations, boundary reconstructions, and land acquisitions. Current practices use 2D survey plans and property base maps to manage and communicate underground RRRs, with some textual notations as well as attributes providing temporal information. However, these methods have limitations, and studies have explored the use of 3D models to address these issues, though they often neglect the temporal aspects. Some studies have investigated 4D cadastre (3D cadastre + time) in different jurisdictions, but these studies mainly remain at a conceptual level for above-ground parcels and buildings. CityGML 3.0 is a data model that provides 3D geometries, topologies, semantics, and entities for modelling temporal aspects such as representing dynamic data and time series and maintaining spatial objects’ history and versions. This paper explores the temporal aspects of underground land administration (ULA) and investigates CityGML 3.0 entities for modelling these temporal aspects, with a synthetic prototype implemented as a proof of concept to demonstrate the applicability of a 4D ULA model. The result indicates that considering the temporal aspects of ULA using CityGML 3.0 entities can improve the functionality and capability of a land administration model in managing and communicating ownership information in underground areas.

Semantic 3D city models have been widely used to solve problems that affect the human-built environment. Due to the complexity of the city and its dynamic aspect, these models should allow studying the evolution of a phenomena in real time. Therefore, 3D city models are evolving towards Digital Twin of cities to allow handling the dynamic aspect of the city. For instance, such evolution requires real-time data to be collected using Internet of Things devices (IoT). This kind of dynamic data requires specific tools that should allow its particular exploitation and manipulation. To model the urban environment, CityGML and CityJSON for instance, as international 3D standards, allow creating, storing and exchanging 3D city models in an interoperable way. This paper aims to propose and implement a methodology for integrating real-time data in a city model by using the “Dynamizer” extension of CityGML based on CityJSON. We then have developed a web interface plugin to handle IoT data and their interactive visualization as a first step for future simulations.

This solution allows the understanding of the 3D city model encoded with JSON, also the simplification and the easy comprehension of the Dynamizer extension compared to the CityGML solution which is difficult to decipher. The solution also makes it possible to establish the connection between the physical entities and their digital twins, to follow the evolution of their real characteristics over time, to represent them, analyze them and take the appropriate decision to preserve the components of the territory, to manage it correctly and ensure its resilience and sustainability.