## 3 Proposed Procedure
### Vector-Based 3D Object Generation Procedure
#### 3.1.2 Preprocessing of 3D Geospatial Data  
In this study, the collected 2D spatial information data and Digital Elevation Model (DEM) data underwent a series of preprocessing steps to ensure consistency across all datasets. The first step involved the conversion and unification of coordinate systems to establish a consistent spatial reference framework.  
Initially, the coordinate systems of each dataset, including buildings, administrative districts, road networks, and DEM, were identified and documented. As these datasets originated from different sources, they often used differing coordinate reference systems. To resolve this, all data were converted into a common reference system, EPSG:5186, as specified in Table 6.  
The transformation process was conducted using QGIS 3.34.7 and the GDAL library, ensuring reliable and standardized projection. To minimize potential accuracy loss during this conversion, bilinear interpolation was applied, particularly for raster data such as DEM.  
Following the coordinate system unification, additional preprocessing was conducted to improve the geometric accuracy and efficiency of the spatial data. This phase included noise removal and data refinement across multiple layers, including buildings, roads, administrative districts, and DEM, as shown in Figure 2.  
For building data, refinement involved detecting and correcting incomplete boundaries such as unclosed polygons and inconsistent orientation (e.g., mixed clockwise and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Coordinate System** & **Type** \\ \hline
1 & Korean 1995 & 4166 \\
2 & WGS 84 & 4326 \\
3 & NAD83/Washington North & 2855 \\
4 & NAD83/New York Long Island & 2263 \\
5 & Korean1985/Unified CS & 5178 \\
6 & KGD2002/Unified CS & 5179 \\
7 & Korea 2000/Central Belt 2010 & 5186 \\
8 & JGD2011 & 6697 \\
9 & ETS89/UTM zone 32N & 25,832 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Coordinate Systems and Their EPSG Codes.  
Figure 2: Workflow of spatial data refinement and clipping for study area preprocessing.  
counterclockwise directions). Extremely small objects were either removed or retained selectively, depending on their relevance to the analysis scope.  
In the case of road data, disconnected road segments were linked to ensure network continuity, while redundant or overlapping features were removed to clean the dataset.  
For administrative district boundaries, similar geometric corrections were applied, resolving issues related to unclosed polygons and inconsistent orientation to maintain spatial integrity and topological accuracy.  
The DEM data also underwent a noise removal step, in which boundary elevation values were adjusted to align harmoniously with surrounding cells. This ensured continuity in elevation representation, which is crucial for accurate \(Z\)-value assignment in 3D modeling.  
To enhance processing efficiency, the datasets were then clipped and reduced to focus on the defined study area. Specifically, a circular region with a 1 km radius centered around Suseo Station in Seoul, Republic of Korea, was extracted from the full dataset. After clipping, the Douglas-Peucker sampling algorithm was applied to simplify the geometries. This technique reduces the number of coordinate points while preserving overall shape fidelity, thereby optimizing memory usage and improving processing speed during 3D object generation.