## 4 Experimental Results and Discussion
### Analysis of Vector-Based Processing Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Category** & **Raster-Based Approach** & **Vector-Based Approach** \\ \hline \(1\) & Processing Type & File-based & Database-based \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{Data Structure} & \multirow{4}{*}{R(Byte), GByte, BByte} & RLB(Byte), GLB (Byte), BLRB(Byte), GRB (Byte), BRB (Byte) \\  & & & RRB(Byte), GRB (Byte), BRB (Byte) \\  & & & RRT(Byte), GRT (Byte), BRT (Byte) \\  & & & RLT(Byte), GLT (Byte), BLT (Byte) \\  & & & Resolution (Byte) \\ \hline \multirow{3}{*}{3} & Average Query & 2.2 s (Loading: & \multirow{3}{*}{0.23 s (retrieval only)} \\  & Time & 2.0 s + retrieval: 0.2 s) & \\ \hline \multirow{2}{*}{4} & Additional & Memory allocation & Additional overhead including \\  & Processing Time & overhead & memory allocation \\ \hline \multirow{2}{*}{5} & Memory & 1.3 GB & 38 MB \\  & Consumption & (including image loader) & (including DB connector) \\ \hline \multirow{2}{*}{6} & \multirow{2}{*}{File Size} & \multirow{2}{*}{12 MB} & \multirow{2}{*}{52 MB} \\  & & & -Adaptive size per region \\  & & -Small file size per pixel & -Resolution-independent \\  & & unit & transmission bandwidth \\  & & -High-speed processing & -Supports decimal-level precision \\  & & -Fast color retrieval & -Supports hierarchical LoD \\  & & & structure for multi-resolution \\  & & & representation \\ \hline \multirow{4}{*}{8} & \multirow{4}{*}{Disadvantages} & \multirow{4}{*}{-High bandwidth} & \multirow{4}{*}{-Large file size per pixel unit} \\  & & & high-resolution images \\  & & -Fixed size per region & -Larger file size per pixel unit \\ \cline{1-1}  & & -Integer-based data & -Slower processing speed \\ \cline{1-1}  & & -Representation & -Slower color retrieval \\ \cline{1-1}  & & -Requires separate files for & \\ \cline{1-1}  & & LoD (mipmap) & \\ \cline{1-1}  & & configuration & \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparison of Raster-Based vs. Vector-based Processing. Across all tests, the vector-based approach consistently outperformed the raster-based method in terms of memory efficiency and scalability. While both methods produced similar visualization results, the vector-based technique proved superior in managing large datasets with minimal performance loss. To further validate the effectiveness of the vector-based approach, spatial accuracy and shape consistency were analyzed using comparative evaluations between 2D spatial data and 3D-generated objects. The evaluation focused on measuring coordinate consistency, placement accuracy, and shape similarity to ensure that the transformation process maintained high fidelity to the original 2D data. The results, summarized in Table 3, confirm that the vector-based approach achieves an average shape similarity index (SSI) of 0.92 and maintains positional accuracy within an acceptable error range of 80 m, demonstrating its reliability in spatial data processing Table 17.  
These findings reinforce the advantages of the vector-based technique, confirming not only its computational efficiency but also its ability to maintain high spatial accuracy when transforming 2D data into 3D objects. This makes it an optimal solution for large-scale spatial data processing applications where both performance and accuracy are critical.