# GAN-Based Map Generation Technique of Aerial Image Using Residual Blocks and Canny Edge Detector
## 4 Experiments
### Performance Analysis and Comparisons
This is likely because Canny edge detection emphasizes structural features (edges), potentially causing variations in overall color distribution. In all model configurations, the LPIPS value is low, indicating that the proposed model generates map images most similar to the original. Figure 2 illustrates map images generated by related studies and the proposed +method, corresponding to \(a_{real}\) and \(m_{real}\). Pix2 Pix [6] exhibits a poor representation of highways, while CycleGAN [7] focuses excessively on constructing roads, resulting in significant differences from \(m_{real}\). The proposed method closely approximates the original image in more straightforward scenarios, as shown in the first row of Figure 2. However, in the more complex situations depicted in the second row of Figure 2, although the proposed method performs better than those in related studies, its ability to accurately represent the map is relatively lower. In the third row, the results of Pix2 Pix [6] and the proposed method show no significant differences. However, in the fourth row, the proposed method demonstrates superior performance in generating the orange roads. In contrast, CycleGAN [7] fails to account for the green and orange areas. For the fifth row, while the color differences between Pix2 Pix [6] and the proposed method are minimal, the proposed method exhibits sharper building generation capabilities. Lastly, the results in the sixth row include an area with water. Here, CycleGAN [7] generates land, resulting in entirely different results. When comparing Pix2 Pix [4] and the proposed method, the proposed method performs better in generating white roads. SMAPGAN [16] focuses primarily on preserving detailed features. While the generated results for buildings are similar to the ground truth, discrepancies are observed in terms of color and roads. In some cases, roads are generated in areas where they do not exist, or highways are omitted, leading to incorrect outputs. Therefore, apart from building generation, SMAPGAN [16] demonstrates suboptimal performance, as evidenced by the quantitative evaluation in Table 4, which shows low performance metrics. Through the results presented in Figure 2, we can directly compare the results of the proposed method with those of related studies on map transformation. This comparison demonstrates that the proposed method exhibits superior performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Residual Block** & **Edge Loss** & **PSNR**\(\uparrow\) & **SSIM**\(\uparrow\) & **LPIPS**\(\downarrow\) \\ \hline Pix2 Pix [6] & X & X & 27.173 & 0.715 & 0.372 \\ CycleGAN [7] & X & X & 18.855 & 0.476 & 0.667 \\ SMAPGAN [16] & O & X & 18.587 & 0.464 & 0.674 \\  & X & O & 27.013 & 0.720 & 0.437 \\ Ours & X & O & 27.039 & 0.721 & 0.434 \\  & O & X & 30.247 & 0.797 & 0.317 \\  & O & O & **30.735** & **0.806** & **0.304** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluations and comparisons of performance. Figure 3 illustrates the map images generated using the proposed method. When compared with conventional approaches, the proposed method demonstrates superior performance in generating maps. Elements like houses, forests, roads, and oceans are depicted with greater clarity. However, in some instances, blurred edges or disconnected roads can be observed. This phenomenon occurs due to the inherent limitations of the original aerial imagery, where edge details are not well represented, such as areas obscured by trees. As a result, the model must infer the boundaries on its own in the absence of clear edge features, leading to these occasional artifacts. These artifacts highlight the limitations of aerial imagery as a data source, especially in situations where feature representation is constrained. Despite these challenges, the proposed method successfully generates map images from the aerial imagery.