## 1 Introduction  
In the field of satellite remote sensing, detecting cloud masks, including shadows, is crucial. This is not only vital for analyzing atmospheric cloud properties [1, 2] but also significantly impacts higher-value-added products related to the land surface, such as vegetation indices, surface reflectance, water, and land use [3, 4, 5]. Clouds and their shadows limit the potential applications of satellite imagery in extracting accurate land surface information [6]. In addition, cloud detection is also one of the key variables for accurately estimating renewable solar energy outputs [7, 8]. With the advent of the New Space era characterized by the launch and utilization of numerous satellites, the number of researchers who have increasingly focused on extracting clouds and cloud shadows from satellite imagery has markedly increased recently [9]. Therefore, improving our ability to accurately detect clouds and cloud shadows is crucial for ensuring the quality of satellite products and for successfully achieving objectives in various fields of application, including renewable solar energy potential. The initial stages of developing cloud detection algorithms are focused on simple thresholding techniques of multispectral satellite bands, known as rule-based classification [10; 11]. These methods utilize the physical properties of clouds in different bands, particularly the visible and infrared channels. With advances in satellite technology, researchers have developed rule-based cloud detection algorithms to leverage higher spatiotemporal resolution and more spectral bands. The MODIS cloud mask product [12] utilizes a variety of tests, including brightness temperature differences, reflectance thresholds, and spatial variability, to enhance cloud detection accuracy. The most well-known cloud detection method for Landsat satellite imagery is Fmask [6; 13; 14], which achieves high accuracy in most circumstances, even for mountainous areas and bright land surfaces. Despite these advancements, detecting thin cirrus clouds and distinguishing cloud shadows remain challenging. Furthermore, Fmask requires auxiliary data such as elevation, along with imagery from all channels, and its computational process can take several minutes, making it difficult to apply to large-scale satellite datasets. Traditional rule-based thresholding techniques for cloud detection have evolved over time, demonstrating their potential to improve accuracy through key spectral indices. For example, [PERSON] et al. (2023) utilized TOA reflectance, NDVI, and several derived values to classify clouds, applying fixed thresholds to distinguish clouds from vegetation and other land surface features [15]. This approach effectively reduced the false detections caused by similar spectral characteristics. However, these methods still rely on manually set thresholds, which limits their adaptability under varying atmospheric and surface conditions. As the need for robust detection systems grew, machine learning and deep learning methods gained traction [16; 17; 18], improving cloud detection by learning complex patterns and better distinguishing clouds, shadows, and the Earth's surface. Despite these improvements, these methods require large amounts of labeled training data. Building on the potential of deep learning, [PERSON] and [PERSON] (2014) developed a neural-network-based model that utilizes both the spectral band data and pre-computed spatial features from the images to identify clouds and their shadows [19]. [PERSON] et al. (2019) extended this by developing a model that uses convolutional neural networks (CNNs) [20], which can process 3D inputs and simultaneously integrate both spatial and spectral information. This approach has demonstrated reliable performance in cloud detection [16; 21]. As a result, numerous cloud detection training datasets have been established to support the development of cloud detection models for various satellites.