## 1 Introduction
These datasets vary in their data processing levels: some are derived from Top-of-Atmosphere (TOA) reflectance or radiance, while others use digital numbers (DNs) from multispectral bands. For instance, cloud detection training datasets from satellites such as Sentinel-2A and 2B (Sen2 Cor) [10], Landsat 8 (L8-Biome, L8-SPARCS, L8-95 Cloud [22]; [[https://landsat.usgs.gov/landsat-8-cloud-cover-assessment-validation-data](https://landsat.usgs.gov/landsat-8-cloud-cover-assessment-validation-data)]([https://landsat.usgs.gov/landsat-8-cloud-cover-assessment-validation-data](https://landsat.usgs.gov/landsat-8-cloud-cover-assessment-validation-data)) (accessed on 7 April 2025)), and PeruSat [23] are provided as TOA reflectance or radiance, which are radiometrically corrected products. In contrast, other studies provide training datasets in a DN (Digital Number) format for deep learning-based cloud detection models. For instance, [PERSON] et al. (2022) evaluated the performance of cloud detection techniques using a modified DeepLabV3+ model with heterogeneous sensors [21], such as the Landsat and KOMPSAT-3 satellites. They found that, despite improvements to the DeepLabV3+ network, which allowed the model to extract and learn channel features from two distinct training datasets, the Jaccard index for clouds remained low due to differences in the datasets. Specifically, the KOMPSAT-3 dataset was distributed in a DN format, while the Landsat L8-Biome training dataset consisted of radiometrically calibrated image products. In other words, even though the deep learning model applied normalization, training on DN data from KOMPSAT-3 and applying the model to radiometrically calibrated Landsat data lowered accuracy. The KOMPSAT-3 data were used without additional radiometric correction because the Korea Aerospace Research Institute (KARI) provided the training datasets in a DN format rather than in TOA (Top-of-Atmosphere) reflectance or radiance. Although the normalization process of the matchup dataset was expected to reduce the discrepancies between the datasets, it proved insufficient in this case. The absence of standardized training datasets has led to a lack of clear criteria for determining the optimal data format for training deep learning models. While several studies have demonstrated that deep learning approaches using either digital numbers (DNs) or Top-of-Atmosphere (TOA) reflectance provide more reliable cloud masking results compared to those of the physical or empirical models, inconsistencies in data formats can affect model training and performance evaluations. In conventional cloud mask algorithms, the input for cloud detection should ideally be TOA reflectance rather than DN values to reduce radiometric differences between images [6, 24, 25]. However, radiometrically calibrated satellite data have not always been applied in deep learning algorithms, as mentioned above. The existing deep learning models are considered robust to pixel value variations due to their complex architecture; however, the impact of converting DNs to TOA reflectance or using various derived indices on cloud segmentation accuracy remains uncertain. Thus, a systematic evaluation is required to determine the impact of these transformations on model performance. However, it is essential to conduct a comprehensive evaluation to establish whether these transformations are necessary for reliable cloud segmentation. Additionally, the lack of defined criteria for developing training datasets in DNs or radiometrically calibrated formats from satellite data may affect other fields, such as building segmentation, road detection, and change detection. Therefore, further analysis is needed to evaluate the performance of deep learning models when radiometric corrections are applied or omitted. Also, this would enhance the development of deep learning-based cloud detection models.