For ground-based optical telescopes, automated planning and control systems play a vital role in ensuring efficient acquisition of scientific data. These systems rely on All-Sky Cameras (ASC) to capture real-time sky conditions, including sky obscuration and lunar position. We address the task of assessing sky conditions under ambient nighttime illumination and in the presence of optical artifacts such as lens flares. Seeking to develop a solution based on a neural network model, we are faced with a lack of real nighttime cloud segmentation benchmark datasets. To address this, we propose an Augmentation and Transformation for Nighttime Cloud Segmentation in All-Sky Camera Images (AT-NCS) that converts existing daytime and dusk cloud segmentation datasets into a synthetic benchmark simulating realistic nighttime observing conditions. Our approach incorporates key challenges such as night sky glow, and optical artifacts (e.g., lens flare), thereby providing a training and evaluation framework for ASC-based observation systems. This work aims to enhance the reliability and value of autonomous observatory operations under diverse nighttime environments.