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Building Digital Twin Data Model based on Public Data

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This study aims to propose a method for constructing basic digital twin data in South Korea by adhering to international standards and utilizing publicly available data. Specifically, the study focuses on designing and proposing a digital twin data model for buildings, which are the most applicable among the basic digital twin data. To achieve this, the first section provides essential background information, introduces concepts and requirements related to digital twin basic data, and offers a brief overview of CityGML. The second section explains the methodology and data used in this study. The third section presents the main findings: the selection of public data (buildings) for constructing digital twin basic data, the mapping process using CityGML, and the creation of UML diagrams. The fourth section discusses these findings. Finally, the conclusion and suggestions for future research are provided. This approach enhances the accuracy of building-related digital twin data and supports the use of digital twin services in both public and private sectors by enabling various spatial analyses.
Title: Building Digital Twin Data Model based on Public Data
Description:
This study aims to propose a method for constructing basic digital twin data in South Korea by adhering to international standards and utilizing publicly available data.
Specifically, the study focuses on designing and proposing a digital twin data model for buildings, which are the most applicable among the basic digital twin data.
To achieve this, the first section provides essential background information, introduces concepts and requirements related to digital twin basic data, and offers a brief overview of CityGML.
The second section explains the methodology and data used in this study.
The third section presents the main findings: the selection of public data (buildings) for constructing digital twin basic data, the mapping process using CityGML, and the creation of UML diagrams.
The fourth section discusses these findings.
Finally, the conclusion and suggestions for future research are provided.
This approach enhances the accuracy of building-related digital twin data and supports the use of digital twin services in both public and private sectors by enabling various spatial analyses.

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