## 1 Introduction
### Research Objectives and Scope  
This study aims to propose an optimized technique for generating Level of Detail 1 (LoD1) 3D objects in a digital twin environment and converting them into the CityGML 3.0 standard format. The study area was set within a 1 km radius of Suseo Station in Seoul, utilizing Shapefile-based 2D spatial data to analyze the efficiency of 3D object generation and texture mapping. The data used in this study were collected in various formats, including orthoimages, satellite images, aerial photographs, buildings, roads, administrative district, and DEM data, as provided by the national operating platform [7]. The key information is as follows in Table 1.  
The primary scope and objectives of this study are as follows:  
1. Optimization of 3D Object Generation Process Efficiency This study generates 3D objects by integrating high-resolution Digital Elevation Models (DEMs) with Shapefile-based 2D spatial data [8], including buildings, roads, and administrative districts. To enhance processing speed and reduce file size, a structured triangular mesh is generated using constrained Delaunay triangulation (CDT), which is then combined with the Douglas-Peucker algorithm to develop an efficient data processing method [9; 10].
2. Enhancement of Texture Mapping Quality High-resolution textures are applied to the generated 3D objects using orthoimages (TIF files). To improve texture alignment accuracy, this study proposes a UV mapping approach utilizing the Wise Interpolated Texture (WIT) technique. The WIT technique aims to reduce texture discontinuities and minimize lighting inconsistencies through interpolation and optimization of texture coordinates. This method enables smoother transitions and sharper details, enhancing accuracy and visual consistency compared to conventional UV mapping techniques.
3. Optimization of Vector-based Data Processing and Memory Usage Efficiency To address memory consumption issues arising from large-scale datasets, an efficient algorithm was designed. By incorporating parallel processing techniques and utilizing  
\begin{table}
\begin{tabular}{c c c} \hline
**No** & **Name** & **Description** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Building Data} & Building name, number of above-ground floors, number of underground floors, coordinates, usage, etc. \\  & & Road name, start point, end point, base interval, length, coordinates, etc. \\ \hline \multirow{2}{*}{3} & Administrative District & Administrative division name, district code, coordinates, etc. \\  & Data & coordinates, etc. \\ \hline \multirow{2}{*}{4} & DEM Data & Includes minimum and maximum X, Y values \\ \hline \multirow{2}{*}{5} & Orthoimages Data & Includes minimum and maximum X, Y values \\ \hline \end{tabular}
\end{table}
Table 1: Data Formats Used.  
the CityGML Validator tool [11], the proposed approach reduces resource consumption during data transformation and validation while improving processing speed.  
As a result, this study focuses on generating interoperable 3D models that comply with the CityGML 3.0 standard, making them applicable across various digital twin platforms. Furthermore, it aims to propose efficient processing techniques for large-scale spatial data, ensuring optimized data handling and integration.