# GAN-Based Map Generation Technique of Aerial Image Using Residual Blocks and Canny Edge Detector
## 2 Related Works
### Generation of Aerial Image to Map  
SAM-GAN [13] addresses a similar topic as the proposed method, focusing on translating aerial images into maps. SAM-GAN generates translated map images by combining the outputs of an encoder that extracts style information from map images and an encoder that extracts content from aerial images. This approach effectively integrates the stylistic elements unique to maps with the content of aerial images, enhancing cartographic representation. However, due to its reliance on combining content and style features, it may face limitations in preserving fine details and detecting critical boundaries of geographical elements within the translated map images. To overcome these challenges, the proposed method incorporates Residual Blocks and a modified Canny edge detection algorithm, which aim to enhance the preservation of terrain details and boundaries in map generation. CannyGAN [14], another related study, integrates the Canny edge detector within the GAN architecture, sharing significant commonalities with the proposed method. It uses a disentangled structure to separate content and style domains, enabling effective edge detection within thermal images, which are characterized by heat distribution rather than standard visual features. By separating content and style, it is able to emphasize edges more distinctly. However, the additional step of disentangling content and style introduces complexity that may hinder detail preservation and high-resolution image translation. In contrast, the proposed method modifies the Canny edge detection algorithm to utilize convolutional operations, incorporating it directly as a loss function. This approach avoids the need for complex disentanglement processes while efficiently enhancing major geographical boundaries within aerial images. By using edge detection in the loss function, the proposed method optimizes map generation, focusing on detail preservation. Thus, while SAM-GAN [13] and CannyGAN [14] share the same goal of image-to-image translation for map generation, both approaches exhibit limitations in achieving precise translation results. The proposed method addresses these limitations by leveraging residual structures and edge detection to ensure more accurate representation of critical geographical features and boundaries within the generated maps, offering a distinct and refined approach to map generation. Although it is not available as open-source, preventing direct experimentation, it can be emphasized that this topic is highly significant within the related field. [PERSON] et al. [15] proposed a map generation approach that utilizes both a discriminative module and a creative module to preserve geographical information and detailed features in aerial imagery. In this approach, the discriminative module classifies semantic information for each pixel in the aerial image to extract accurate geospatial data, leveraging DeepLabv3+ to enhance semantic segmentation performance for high-resolution map generation. Following this, the creative module employs a conditional GAN (cGAN) to transform semantic information into a map style, using a U-Net-based generator architecture to improve visual quality. This module comprises both a global and a local generator, enabling balanced representation of overall structure and fine details. Additionally, a multi-scale discriminator is applied to optimize the visual quality of generated maps across various resolutions. While this paper adopts a similar approach to [PERSON] et al. [15] in preserving geographic details when transforming aerial imagery into maps, it distinguishes itself by incorporating the Canny edge detector as a loss function to more precisely retain boundary information. Although this approach effectively enhances semantic information and visual quality, it lacks a dedicated edge preservation method, potentially resulting in blurred or undefined geographical boundaries.