The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVIII-4-2024
https://doi.org/10.5194/isprs-archives-XLVIII-4-2024-525-2024
https://doi.org/10.5194/isprs-archives-XLVIII-4-2024-525-2024
21 Oct 2024
 | 21 Oct 2024

A Geodatabase Design for the Development of a Digital Twin for Urban Environments: A Case Study from Turin, Italy

Yogender Yadav, Elisabetta Colucci, Piero Boccardo, and Sisi Zlatanova

Keywords: 3D City Models, Geodatabase, Urban Digital Twins, Point Clouds, Urban Planning

Abstract. 3D City models are essential for urban planning, accurately visualizing, analyzing, and simulating urban environments. They find applications in various fields like AEC (Architecture and Construction), urban and transportation planning, development and conservation processes, energy systems monitoring, and many more. Extensive datasets for the 3D City Models need to be organized following international standards to make them reusable and sharable for different stakeholders. Designing a Geodatabase (GeoDB) for a 3D city model is crucial for data management as it is helpful in the streamlined visualization and analysis of the city's features and relations among its objects. Over the years, 3D city models have evolved into Urban Digital Twins, offering dynamic real-time simulations of cities and even better management and analysis of urban processes. This transition from 3D city models to UDTs enhances decision-making by providing detailed insights into the dynamics of urban systems, enabling better urban management and planning. This research concentrates on building a GeoDB to support the UDT. The paper discusses the data acquisition, processing, and integration methodologies.
Additionally, it highlights the significance of utilizing advanced remote sensing technologies such as aerial LiDAR and aerial photogrammetry to enhance the digital twin’s quality and richness in detail. The semantics of the built environment datasets are clarified and strictly defined for the most important UDT features, such as buildings, roads, trees, and other features. This will ensure everyone can interpret the data similarly, leading to better analysis and decision-making.