Towards a multi-database CityGML environment adapted to big geodata issues of urban digital twins

Abstract. This paper investigates the challenges of implementing the CityGML standard within database environments for managing urban digital twins (UDT), with a particular focus on addressing big geodata issues. CityGML stands as a critical standard for representing and exchanging 3D urban models and thematic data, essential for effective urban planning and infrastructure management. However, integrating CityGML into databases poses challenges due to the dual requirements of geospatial and semi-structured data. While the former imposes a certain rigor in its formalism, the second benefits from more flexibility. Through a comprehensive review and benchmarking of existing database implementations, including both new distributions and those often use, 3DCityDB, CJDB, Measur3D, and Cerbere, this paper proposes a novel approach towards a multi-database CityGML environment tailored to the specific needs of UDT. The proposed solution leverages the strengths of both relational and NoSQL databases, offering a flexible and scalable architecture while ensuring data consistency, geospatial capabilities and compliance with the CityGML schema. The research hypothesis suggests integrating Cerbere, a middleware for CityGML schema compliance and transaction validation, with 3DCityDB and Measur3D (MongoDB). This approach aims to demonstrate the feasibility of managing advanced 3D data operations based on the CityGML model (3DCityDB) and scalability for big data like IoT (Measur3D) within a multi-database environment. The paper contributes to the advancement of UDT by providing a comprehensive solution for managing diverse data types, facilitating more effective urban planning, infrastructure management, and sustainable development initiatives in the context of smart cities.