Data integration across urban digital twin lifecycle: a comprehensive review of current initiatives

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ABSTRACT Challenges related to data integration and interoperability were raised recently under the auspice of the Urban Digital Twin (UDT). This new paradigm shows its potential to address current city challenges. However, to maximize its outcomes at the city scale, we should tackle the fundamental issues related to data integration. Indeed, various Digital Twin (DT) frameworks are developed in practice. Their implementations led to the identification of three main levels of data integration. The first level involves the extension of the data model to handle new information. The second level supports data by default, and the data needs to be transformed to meet the model requirements. The third level performs the integration at the front-end level with the help of system architectures. The aim of this work is to analyze, illustrate, and guide the effectiveness of different data integration approaches. This exploratory review unpacks the levels of integration according to the corresponding UDT lifecycle phases (i.e., creation, use, and update phases). It highlights the challenges and potentialities of data integration levels and offers the DT designer conceptual guidelines related to data integration. Furthermore, current and theoretical data integration scenarios are extracted and investigated, considering several types and sources of data. This research provides a comprehensive analytical framework for data integration within UDTs, where some of the current operational UDT are examined based on the various integration levels of life cycle data. While the state-of-the-art identifies data integration as a major challenge for the full implementation of UDT, it is not explored in depth, and the integration is only addressed from a case study-specific perspective, according to the data availability and the UDT requirement. Hence, this framework provides a generic and urban application-independent overview of the different levels of data integration based on the UDT lifecycle inspired by the Spatial Data Infrastructure lifecycle. This article provides first conceptual insights of data integration levels to build, use, and update UDT. However, from a practical perspective, the list of UDT initiatives used to illustrate the work is not exhaustive, and future initiatives should be documented. Furthermore, the current emphasis is on the creation and use phases of the lifecycle, which lacks a concrete illustration of the update phase. Indeed, it limits the practicability of the data integration levels in the maintenance phase.