# GAN-Based Map Generation Technique of Aerial Image Using Residual Blocks and Canny Edge Detector
## 6 Conclusions  
This study introduced a GAN-based method for generating high-quality maps from aerial imagery. The proposed model effectively incorporates Residual Blocks to reduce noise while preserving essential structural features and employs a Canny edge detection-based loss function to enhance edge preservation during the map generation process. The experimental results demonstrated that the proposed method outperforms traditional approaches, generating maps with clear representations of houses, forests, roads, and coastlines. However, several limitations were identified in this study. The model struggled with accurately generating maps in complex terrains, such as mountainous regions and dense road networks. These issues stem from the limitations inherent in aerial imagery, where certain areas, such as roads, may be occluded by trees or other obstacles, resulting in incomplete or blurred edge information. In such cases, the GAN model is forced to infer the boundaries, leading to occasional artifacts like discontinuous roads or blurred edges. To address these limitations, future work should incorporate a more diverse set of aerial images captured under different environmental conditions. The current dataset primarily consists of images captured under specific weather conditions and during certain times of the year. Incorporating data captured during different seasons and under various climatic conditions would allow the model to generalize better. For instance, roads may be obscured by snow in winter, while dense foliage may obstruct visibility in summer. By including such diverse data, the model can be trained to handle a wider range of scenarios and produce more robust and generalized map outputs. Moreover, additional techniques are required to handle the accurate generation of maps in challenging terrains, such as mountainous regions and dense urban road networks. One potential approach is to incorporate multi-resolution aerial imagery, which would provide higher detail for specific regions, or to integrate 3D terrain data with aerial imagery. The use of 3D terrain data, which includes information on elevation and surface contours, can provide additional context to the GAN model, enabling it to generate more accurate representations of complex geographies, especially in regions where elevation changes are significant. In addition, the map generation process can be further refined by adopting task-specific training strategies. For instance, if the goal is to generate maps for transportation network design, priority should be placed on accurately preserving road connectivity and boundaries. In such cases, the use of specialized loss functions that emphasize road networks or incorporating a road recognition module could improve performance. On the other hand, maps intended for environmental conservation efforts may require more accurate representations of forests, rivers, and other natural features, necessitating a different approach to feature extraction and training. In conclusion, the proposed GAN-based method successfully generated high-quality maps from aerial imagery, demonstrating superior performance in noise reduction and structural feature preservation compared to existing methods. However, to further enhance the model's capability, future research should focus on expanding the dataset to include aerial images captured under diverse environmental conditions, as well as integrating additional data sources, such as 3D terrain information, to improve the model's performance in complex terrains. Task-specific optimization techniques should also be explored to tailor the map generation process to different application domains, such as transportation planning or environmental monitoring. **Author Contributions:** Conceptualization, J.S. and S.K. ; methodology, J.S. ; software, J.S. ; validation, J.S. ; formal analysis, J.S. and S.K. ; investigation, J.S. ; resources, S.K. ; data curation, J.S. ; writing--original draft preparation, J.S. ; writing--review and editing, S.K. ; visualization, J.S.