# GAN-Based Map Generation Technique of Aerial Image Using Residual Blocks and Canny Edge Detector
## 2 Related Works
### Image-to-Image Translation  
Pix2 Pix [6] is a study based on Conditional GANs, where the generator is trained to transform and generate images using actual images as inputs, rather than random noise. The generator in Pix2 Pix [6] adopts a U-Net architecture, effectively preserving detailed structures of the input image, while the discriminator employs a PatchGAN architecture, assessing the realism of each patch within the image. This architecture is particularly well-suited for image-to-image translation tasks and produces more realistic transformation results compared to previous GAN models that generate images from noise. CycleGAN [7] does not require paired image sets, instead learning to translate between two distinct domains. This approach is especially useful in scenarios where paired datasets are difficult to obtain. It incorporates a backward function to restore the translated image back to its original form, thereby maintaining consistency between the original and translated images. Additionally, it introduces a Consistency Loss function to ensure alignment between the generated and original images, allowing for effective cross-domain transformation without paired data. However, Pix2 Pix [6] and CycleGAN [7] face limitations in generating precise, high-resolution maps due to difficulties in capturing detailed information. When generating maps from aerial images, it is essential to preserve key geographical features, including the precise delineation of roads and building edges. Thus, this study introduces a novel approach to overcome these limitations in existing research, addressing critical issues related to detail preservation and noise reduction required for map generation. By doing so, it effectively preserves essential features while eliminating superfluous details, thus enhancing the accurate representation of geographical information. StarGAN [8] proposes a method for translating images across multiple domains using a single generator, addressing the limitations of fixed-domain translation. StarGAN [8] primarily focuses on translating facial images, which differentiates it from the proposed method in terms of subject matter. However, its ability to perform multi-domain image translation lays a foundational basis for research in image-to-image translation that extends beyond a single domain, offering flexibility for various transformation tasks. CoMoGAN [9] introduces Functional Instance Normalization and Disentanglement of Residual Block structures into the GAN architecture, enabling continuous image translation. The incorporation of residual structures allows for smoother and more natural transitions during complex transformations. In the proposed method, similar residual block structures are utilized to preserve intricate details and reduce noise, which is crucial for generating maps from aerial images. [PERSON] et al. [10] proposed a Recurrent Transformer Network for the restoration of damaged old film images, converting them back into high-quality photographs. This study achieved significant restoration performance by leveraging hidden information between adjacent frames, thereby ensuring temporal consistency during frame-by-frame restoration. While the subject and objectives differ from the proposed method, both share a focus on image restoration within the broader context of image-to-image translation. The key distinction of the proposed method lies in its focus on preserving critical geographical elements for map generation from independent aerial images rather than utilizing temporal information from adjacent frames. Hourglass Block-GAN [11] employs a U-Net-based hourglass structure to restore highly compressed images back to their original high-resolution state. This study also aims to achieve image-to-image translation through GANs, focusing on recovering lost details. However, while Hourglass Block-GAN concentrates on restoring compressed images, the proposed method focuses on generating maps from aerial images.