# GAN-Based Map Generation Technique of Aerial Image Using Residual Blocks and Canny Edge Detector
## 2 Related Works
### Research Across Various Fields Related to Aerial Image  
[PERSON] et al. [17] proposed a model for detecting multiple targets in UAV aerial images, enhancing the YOLOv8-s model's 'neck' section with a Bi-PAN-FPN structure to reduce the misdetection and false detection of small objects. This structure enhances the accuracy of detecting small objects in aerial images, focusing specifically on object detection in UAV imagery. In contrast, our research is centered on accurately representing geographic information for the purpose of map generation. DiffusionSat [18] enhances existing diffusion models to generate realistic remote sensing data, utilizing large-scale public satellite image datasets and metadata, such as geographic location, to create realistic satellite images. This model supports various generative tasks, including multi-spectral super-resolution, temporal generation, and in-painting. Although our research also focuses on image generation, it is distinguished by its emphasis on map generation rather than aerial image generation, setting our approach apart from DiffusionSat [18].  
D2 ANet [19] introduces a dual-temporal satellite imagery approach for detecting building locations and enabling multi-level change detection, focusing on feature channel differences between pre- and post-disaster images and activating change-sensitive channels to learn global change patterns. While this approach is beneficial for detecting terrain changes in map generation, our study focuses on generating maps from single-timepoint images, providing precise boundary and geographical element representation. [PERSON] et al. [20] proposed a framework combining spatial geometric feature data with aerial images to estimate building features and generate visually reliable building outlines. This method uses a deep network configuration involving generative modeling, segmentation, and adversarial learning to compensate for degraded aerial image quality. Though our study also employs a generative model, it emphasizes preserving geographic boundaries for map generation and uniquely integrates edge detail preservation as a loss function based on Canny edge detection.  
[PERSON] et al. [21] proposed a method for learning effective representations from unlabeled satellite image data using self-supervised learning, introducing a new loss function and a sampling method across diverse geographical regions to enhance semantic segmentation and change detection. While our research shares the objective of achieving high performance, our method is specialized for preserving boundaries and details critical for map generation. [PERSON] et al. [22] presented a cloud removal method for satellite images. This model utilizes a convolutional neural network to extract cloud transparency information and restore surface details in thin cloud regions. While our research does not address cloud removal, it shares the objective of enhancing terrain detail preservation to improve map generation accuracy. [PERSON] et al. [23] introduced P2 Cnet, a model for extracting roads from aerial images and partial road maps, filtering out irrelevant information and focusing on absent road pixels. Although our research also emphasizes boundary detection and map generation, it offers a more comprehensive approach by generating detailed maps that encompass multiple geographical elements beyond roads alone.  
In summary, previous research [17; 18; 19; 20; 21; 22; 23] provides various approaches to achieve specific objectives within satellite and aerial image analysis. However, our study contributes a unique approach focusing on map generation through detail preservation and boundary accuracy, delivering comprehensive geographic information.