## 2 Materials  
This study uses two datasets. The first is the LoveDA dataset [9], which is the primary dataset used to test the single dataset improvement and also serves as the host dataset for dataset fusion. The second dataset is a custom dataset based on the KOMPSAT-3A images, which is used as a guest dataset for the dataset fusion. The second dataset is described in the text as CITY-20, meaning that it was produced in 2020, and most of the scenes are set in city areas.  
The LoveDA dataset comprises 5987 pairs of images and corresponding masks. The images were collected from Google Earth in urban and rural China, and the masks are divided into seven classes: background, buildings, roads, barren, agriculture, forest, and water. Google Earth images come from a variety of sources, making it difficult to specify the sensors used to capture the images. However, the images in this dataset have a spatial resolution adjusted to 30 cm per pixel and consist of three bands: Red, Green, and Blue, with 8 bits per pixel of quantized data. While the images are georeferenced, allowing us to pinpoint their location, we cannot determine the exact time when each picture was taken. Figure 1 shows some samples of the dataset.  
We adjusted the spatial resolution to 1 m per pixel to harmonize various image sources from Google Earth and maintain consistency with the CITY-20 dataset. Furthermore, we applied sample augmentation techniques to enhance the diversity of the data (Figure 2). Although sample augmentation can enhance performance [28], many geometric transformations are not suitable for the small patches in the LoveDA datasets. Therefore, we implemented a straightforward approach by rotating the patches 90 degrees three times. Additionally, we converted the input patch's color model from RGB to YCbCr, added Gaussian noise with a standard deviation of 5.0 to the Cb and Cr components, and then converted it back to RGB. This process adjusts the color without affecting the brightness. As a result, we obtained a total of 23,948 patches.  
The CITY-20 dataset consists of images captured by the KOMPSAT-3A satellite, which provides panchromatic data with a resolution of 0.55 m and multispectral data with a resolution of 2.2 m, including Red, Blue, Green, and Near Infrared (NIR) bands. For this  
Figure 1: Four samples from the LoveDA dataset. On the left are the images, and on the right are the class masks. Each mask is represented using 8-bit raster data along with the color map.  
Figure 2: Sample augmentation conducted on the LoveDA dataset. The first row shows the augmented images, and the second row shows the corresponding class masks.  
study, we utilize the pan-sharpened products, and the NIR band is excluded. The spatial resolution is adjusted to 1 m per pixel to ensure compatibility with the host dataset. The original imagery contains 16 bits of quantized data, which we compress to 8 bits using 1% percentile stretching. The dataset comprises patches from fifteen scenes collected around major cities in South Korea, with imagery sourced from the years 2015 to 2019. The dataset includes 145,287 pairs of images and masks with augmented samples. The class configuration is similar to that of the LoveDA dataset, except for the absence of the barren class (Figure 3). Despite their class similarity, the two datasets differ in annotation style and quality. In the CITY-20 dataset, adjacent small buildings are annotated individually, whereas in the LoveDA dataset, they are merged into a large mask. The CITY-20 dataset has better building and road mask quality, but a large part of the forest mask is missing.