Segmentation and Celestial Mapping of Unobservable Regions in Nighttime All-sky Images for Mephisto Observations 后印本

摘要: Accurate identification of unobservable regions in nighttime is essential for autonomous scheduling and data quality control in observations. Traditional methods—such as infrared sensing or photometric extinction—provide only coarse, non-spatial estimates of sky clarity, making them insufficient for real-time decision-making. This not only wastes observing time but also introduces contamination when telescopes are directed toward cloud-covered or moonlight-affected regions. To address these limitations, we propose a deep learning-based segmentation framework that provides pixel-level masks of unobservable areas using all-sky images. Supported by a manually annotated data set of nighttime images, our method enables precise detection of cloud- and moonlight-affected regions. The segmentation results are further mapped to celestial coordinates through Zenithal Equal-Area projection, allowing seamless integration with observation control systems for real-time cloud-aware scheduling. While developed for the Mephisto telescope, the framework is generalizable and applicable to other wide-field robotic observatories equipped with all-sky monitoring.