# GAN-Based Map Generation Technique of Aerial Image Using Residual Blocks and Canny Edge Detector
## 1 Introduction  
In modern fields such as Geographic Information Systems (GISs), urban planning, and disaster response, accurate and detailed maps play a pivotal role. Maps provide a visual representation of geographic spaces, allowing complex spatial information to be interpreted and utilized for decision-making processes in various domains. In rapidly urbanizing societies, precise mapping data are essential for the planning and development of new residential areas, commercial facilities, and transportation networks. Beyond simple topographical information, these maps support critical decision-making in infrastructure management, transportation planning, resource allocation, and emergency response strategies [1; 2]. Aerial imagery, especially with the aid of UAVs and modern high-resolution sensors, provides a distinct advantage over traditional cartography for generating detailed maps and accurate geographic models [3; 4]. Traditional map-making methods are labor-intensive, time-consuming, and struggle to keep up with the rapidly changing landscape. As urban development accelerates, the need for real-time map updates becomes urgent, yet traditional methods fall short of keeping pace. This delay results in inefficiencies in urban planning, resource management, and transportation network expansion. In emergency response scenarios, the lack of up-to-date map information significantly hampers the ability to deploy resources swiftly. Consequently, there is a growing demand for automated systems capable of rapidly processing spatial data and generating real-time maps that reflect the latest geographical changes [5]. One promising solution is the automated generation of maps from aerial images. Aerial images offer high-resolution, large-scale data covering extensive areas, with detailed information about roads, buildings, and natural landscapes. These attributes make aerial imagery a valuable resource for urban planning and disaster management. Converting aerial images into map representations can dramatically improve both the speed and accuracy of map creation, making it possible to incorporate the most current spatial information. Thus, techniques for automatically mapping aerial imagery have the potential to extend beyond traditional map-making and serve as valuable assets with significant social impact. In recent years, image-to-image translation techniques [6; 7; 8; 9; 10; 11] have gained attention for their applicability in transforming aerial imagery into map representations. Image-to-image translation is a process by which one type of image is converted into another, and in this context, Generative Adversarial Networks (GANs) have shown remarkable effectiveness. A GAN comprises two adversarial neural networks: a generator, which creates new images, and a discriminator, which distinguishes between generated and real images. The adversarial structure of GANs enables them to produce increasingly realistic images, making GANs well-suited for the automatic generation of maps from aerial images [12]. However, applying GANs to aerial-to-map translation presents certain challenges, primarily in preserving critical details such as road networks, boundaries, and other key infrastructural elements. These details are crucial in domains like urban planning, transportation network design, and disaster response. If such details are lost, the accuracy and reliability of the generated maps can be significantly compromised. For example, a map lacking a clear representation of road networks may hinder transportation planning, and indistinct building boundaries can reduce the map's utility during disaster response, where precise infrastructure information is essential. Although existing GAN-based methods have attempted to address these issues, they often fall short of fully preserving essential details, necessitating further research to develop improved methodologies. This study proposes a novel GAN-based map generation approach designed to address these issues.