## 2 Material and Methods
### KOMPSAT-3 and KOMPSAT-3A Dataset Construction Process  
The cloud mask dataset used in this study was provided by the Korea Aerospace Research Institute (KARI) and was designed using a multi-step approach to ensure high reliability and accuracy. Initially, a Land Use Land Cover (LULC) map provided by the European Space Agency (ESA) was utilized to achieve balanced diversity across various land cover types in the KOMPSAT-3 and KOMPSAT-3A datasets. Subsequently, the research team gathered cloud detection training data from satellite imagery that the KARI multi-purpose satellites acquired between 2019 and 2022, focusing on images with cloud cover between 20% and 50%. This selection was made because images with cloud cover exceeding 50% can adversely affect image registration quality, making images with 20% to 50% cloud cover more suitable for dataset construction. Artificial intelligence (AI) techniques initially labeled approximately 20-30% of the data to automate the process. After this, a team of annotation workers, who had undergone specialized training, performed a second round of manual annotations following the KARI's established criteria. The data preprocessing team and the AI model development team further refined the dataset through an iterative review process involving collaboration. This rigorous process ensured the dataset's reliability, making it suitable for cloud mask development and validation for KOMPSAT-3 and KOMPSAT-3A. In this study, the cloud detection dataset consists of multi-purpose satellite imagery, including red, green, blue (RGB) and Near-Infrared (NIR) channel data (as shown in Table 1), along with previously generated cloud detection labels. The cloud mask labels in the dataset are categorized into four classes: clear sky, thick cloud, thin cloud, and cloud shadow. In Figure 1d, the colors used to represent these classes are as follows: red for thick clouds, green for thin clouds, yellow for cloud shadows, and black for clear sky. Clear sky refers to cloud-free areas where the surface is fully visible. Thick clouds are characterized by high reflectance values in optical imagery, completely obscuring surface features beneath them. Thin clouds are commonly observed surrounding thick clouds, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Satellite** & **KOMPSAT 3** & **KOMPSAT 3A** \\ \hline Blue-band wavelength (um) & 450–520 & 450–520 \\ \hline Green-band wavelength (um) & 520–600 & 520–600 \\ \hline Red-band wavelength (um) & 630–690 & 630–690 \\ \hline NIR-band wavelength (um) & 760–900 & 760–900 \\ \hline Resolution (m) & 2.8 & 2.2 \\ \hline Publicly opened cloud & & \\ detection dataset & KOMPSAT-3 & KOMPSAT-3A \\ \hline Sensor & PAN, MSI & PAN, MSI \\ \hline Launch year & 2015 & 2017 \\ \hline Data acquisition period & 3 days & 3 days \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of satellite sensors from KOMPSAT-3 and 3A for cloud masks and whether TOA radiance was applied or not. where surface features such as buildings or vehicles remain partially visible but appear more blurred than in clear sky regions. Additionally, pixels affected by aerosols or fog, which are often difficult to distinguish from clouds in optical imagery, are also classified as thin clouds. Cloud shadow is strictly defined as the shadow cast by clouds, where sunlight is blocked, creating darkened areas on the surface. Shadows caused by terrain or other obstructions are not included in this category. In this study, the multi-purpose satellite images used were provided by the Korea Aerospace Research Institute (KARI) and consisted of a total of 177 images. Of these, we used 130 images for training, 16 for validation, and the remaining 31 for testing. The resolution of each image used for training and validation was approximately 6000 \(\times\) 6000 pixels, with slight variations in size. To address these size inconsistencies, we divided the satellite images into patches of 800 \(\times\) 800 pixels, with a 400-pixel overlap between patches to minimize discontinuities. (Figure 2).