## 2 Material and Methods
### Specifications of Hyperparameters  
Originally introduced by [PERSON] et al. (2016) in their seminal work on semantic segmentation tasks, Dice Loss has proven to be an effective tool for tackling complex segmentation challenges [33]. In cloud segmentation tasks, class imbalance presents a significant hurdle, as the background (clear sky class) typically dominates the pixel distribution, vastly outnumbering the cloud regions. This imbalance often biases models toward over-predicting clear sky areas, yielding decuptively high accuracy metrics while failing to adequately detect clouds, which are the primary objects of interest. Dice Loss mitigates this issue by automatically balancing the foreground (clouds) and background (clear sky) without the need for manual class weight adjustments, a feature that distinguishes it from traditional approaches. This capability allows Dice Loss to outperform conventional loss functions, such as clear sky and multi-class logistic loss, particularly in imbalanced segmentation scenarios where minority classes require greater emphasis. [PERSON] et al. (2016) first demonstrated its effectiveness in medical image segmentation, a domain where similar class imbalance problems such as distinguishing small regions of interest like tumors from large backgrounds are prevalent ([PERSON] et al., 2016). Building on these findings, we extend the application of Dice Loss to cloud segmentation, anticipating comparable advantages in tackling class imbalance due to the structural similarities between these tasks. Recent studies further suggest that combining Dice Loss with pixel-weighting strategies can enhance its performance in satellite imagery, improving the delineation of cloud boundaries--an insight that reinforces its potential in this context. Dice Loss is mathematically derived from the Dice Similarity Coefficient, a metric that quantifies the overlap between the predicted segmentation and the ground truth, as expressed in Equation (1), making it particularly adept at optimizing for the precise segmentation of underrepresented classes like clouds. This derivation ensures that the loss function prioritizes overlap, offering a robust solution for improving detection in imbalanced datasets.  
\[\text{Dice Loss}=1-\frac{2*|GT\cap Pred|}{|\text{GT}|+|Pred|} \tag{4}\]  
GT: ground truth from labeled image;  
_Pred_: predicted cloud detection image from model.  
In addition, we determined the optimal settings for our deep learning model for cloud detection through extensive hyperparameter tuning, as shown in Table 2. Our selection of hyperparameters was guided by prior studies on cloud detection in satellite imagery, particularly those utilizing AI algorithms for Kompsat-3/3A satellite images ([PERSON] et al., 2016). Building upon these existing techniques, we incorporated experimental approaches to further refine the model. We utilized an Adam optimizer with a learning rate of 0.0003, which was adjusted by a scheduling factor of 0.5 to enhance convergence. To maintain consistent updates, the model incorporated a momentum of 0.937, and a weight decay of 0.0005 was applied to prevent overfitting. These parameters were carefully selected to optimize the model's performance in accurately detecting clouds.