Due to the cloud coverage of remote-sensing images, the ground object information will be attenuated or even lost, and the texture and spectral information of the image will be changed at the same time. Accurately detecting clouds from remote-sensing images is of great significance to the field of remote sensing. Cloud detection utilizes semantic segmentation to classify remote-sensing images at the pixel level. However, previous studies have focused on the improvement of algorithm performance, and little attention has been paid to the impact of bit depth of remote-sensing images on cloud detection. In this paper, the deep semantic segmentation algorithm UNet is taken as an example, and a set of widely used cloud labeling dataset “L8 Biome” is used as the verification data to explore the relationship between bit depth and segmentation accuracy on different surface landscapes when the algorithm is used for cloud detection. The research results show that when the image is normalized, the effect of cloud detection with a 16-bit remote-sensing image is slightly better than that of an 8-bit remote sensing image; when the image is not normalized, the gap will be widened. However, using 16-bit remote-sensing images for training will take longer. This means data selection and classification do not always need to follow the highest possible bit depth when doing cloud detection but should consider the balance of efficiency and accuracy.