## 1 Introduction  
Currently, high-resolution aerial imagery is available from a variety of sources, including aircraft, drones, and satellites, allowing for more precise segmentation of land use. Satellite images are widely used because they can be captured regardless of regional restrictions. WorldView, Pleiades, QuickBird, Ikonos, and SkySat are the major satellite series providing high-resolution imagery. As of 2025, four high-resolution optical satellites with a ground sample distance (GSD) of less than 1 m are in operation in Korea. They are the New-space Earth Observation Satellite Constellation No.1 (GSD 1 m), KOMPSAT-3 (GSD 70 cm), KOMPSAT-3A (GSD 55 cm), and Compact Advanced Satellite No.1 (GSD 50 cm). As high-resolution satellite imagery becomes more widely available, a variety of land-use segmentation datasets have been produced, and related models based on these data have been proposed. In the early stages of adopting deep-learning techniques in remote sensing, datasets were created that focused on the segmentation of major land use classes, such as buildings, roads, cropland, water, and forests, based on images collected at specific locations by specific satellites or sensors. The International Society of Photogrammetry and Remote Sensing has released the Potsdam-Vaihingen dataset, which provides aerial imagery with a GSD of 5-9 cm; land-cover labels for six classes: impervious surfaces, buildings, low vegetation, trees, cars, clutter, and background; and a digital surface model, which is still being used to study new network architecture applications [1; 2]. The DeepGlobe dataset comprises seven land-cover classes: urban, agricultural, rangeland, forest, water, wasteland, and unclassified, utilizing imagery captured by DigitalGlobe's satellites at a GSD of 50 cm [3]. The Inria Aerial Image Labeling Dataset provides building masks with aerial imagery at a GSD of 30 cm spanning approximately 810 km\({}^{2}\) of cities in the United States and Europe [4]. Recent datasets focus not only on land-cover masks but also on specific, practical topics. The BONAI dataset is a building segmentation dataset that provides not only roof masks but also footprint masks and offset vectors, clarifying building structural information at off-nadir image acquisition [5]. The LEVIR Multilevel Change Interpretation dataset contains descriptive caption data for changes to roads and buildings, enabling the use of large-scale language models to describe these changes accurately [6]. The Semantic Change Detection Dataset addresses the limitation of binary change detection, which only distinguishes changes in category, by providing an additional non-change mask for multitemporal images at the same location [7]. The French National Institute of Geography and Forestry (IGN) has developed the \"French Land Cover with Aerospace ImageRy\" (FLAIR) dataset, which includes data acquired by multiple sensors at a large scale with temporal shifts, as well as height information from the ground [8]. The Land-Cover Dataset for Domain Adaptive Semantic Segmentation (LoveDA) dataset emphasizes the balance between data acquisition in urban and rural areas [9]. Recent datasets tend to provide more accurate information by providing additional information without limiting the image source, location, or collection time. This indicates that the practical aspect of analytics is becoming increasingly important, and more accurate data is required to build robust models. As more advanced semantic segmentation models and backbone networks emerge in the field of computer vision, many researchers are actively applying popular architectures to remote sensing images with notable success [10]. Some key examples of effective segmentation architectures include the Fully Convolutional Network (FCN) [11], U-Net [12], DeepLab [13], and HRNet [14]. Additionally, there have been substantial advancements in backbone networks that can be paired with segmentation networks, such as ResNet [15], ResNeSt [16], EfficientNet [17], Swin Transformer [18], and ConvNeXt [19].