Satellites equipped with optical sensors capture high-resolution imagery, providing valuable insights into various environmental phenomena. In recent years, there has been a surge of research focused on addressing some challenges in remote sensing, ranging from water detection in diverse landscapes to the segmentation of mountainous and terrains. Ongoing investigations goals to enhance the precision and efficiency of satellite imagery analysis. Especially, there is a growing emphasis on developing methodologies for accurate water body detection, snow and clouds, important for environmental monitoring, resource management, and disaster response. Within this context, this paper focus on the cloud segmentation from remote sensing imagery. Accurate remote sensing data analysis can be challenging due to the presence of clouds in optical sensor-based applications. The quality of resulting products such as applications and research is directly impacted by cloud detection, which plays a key role in the remote sensing data processing pipeline. This paper examines seven cutting-edge semantic segmentation and detection algorithms applied to clouds identification, conducting a benchmark analysis to evaluate their architectural approaches and identify the most performing ones. To increase the model's adaptability, critical elements including the type of imagery and the amount of spectral bands used during training are analyzed. Additionally, this research tries to produce machine learning algorithms that can perform cloud segmentation using only a few spectral bands, including RGB and RGBN-IR combinations. The model's flexibility for a variety of applications and user scenarios is assessed by using imagery from Sentinel-2 and Landsat-8 as datasets. This benchmark can be reproduced using the material from this github link: \url{https://github.com/toelt-llc/cloud\_segmentation\_comparative}.

翻译：配备光学传感器的卫星捕获高分辨率影像，为多种环境现象提供了宝贵见解。近年来，针对遥感领域诸多挑战的研究激增，涵盖从不同景观中的水体检测到山地与地形分割。持续研究旨在提升卫星影像分析的精确性与效率，尤其关注开发用于精准水体、雪及云检测的方法论，这对环境监测、资源管理与灾害响应至关重要。在此背景下，本文聚焦于遥感影像中的云分割。由于光学传感器应用中云的干扰，精准的遥感数据分析面临挑战。云检测在遥感数据处理流程中扮演关键角色，直接影响应用与研究等最终成果的质量。本文考察了七种用于云识别的先进语义分割与检测算法，通过基准分析评估其架构方法并识别性能最优者。为增强模型适应性，重点分析了影像类型与训练中使用的光谱波段数量等关键要素。此外，本研究尝试开发仅需少量光谱波段（包括RGB与RGBN-IR组合）即可完成云分割的机器学习算法。通过采用Sentinel-2与Landsat-8影像作为数据集，评估模型在多种应用场景与用户情境下的灵活性。本基准可通过此GitHub链接复现：\url{https://github.com/toelt-llc/cloud\_segmentation\_comparative}。