Segmentation and Celestial Mapping of Unobservable Regions in Nighttime All-sky Images for the Mephisto Observations
Jian Cui, Guo-Wang Du, Xin-Zhong Er, Chu-Xiang Li, Jun-Fan Hou, Yu-Xin Xin, Xiang-kun Liu, Xiao-Wei Liu

TL;DR
This paper introduces a deep learning segmentation framework that accurately identifies unobservable sky regions in nighttime all-sky images, enabling real-time, celestial coordinate mapping for improved autonomous telescope scheduling.
Contribution
The paper presents a novel deep learning-based pixel-level segmentation method for unobservable sky regions, supported by a manually annotated dataset, and maps these regions to celestial coordinates for real-time observation planning.
Findings
High-precision detection of cloud- and moonlight-affected regions
Seamless integration with observation control systems for real-time scheduling
Framework generalizable to other wide-field robotic observatories
Abstract
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 dataset 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…
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Taxonomy
TopicsSpace Satellite Systems and Control · Impact of Light on Environment and Health · Astronomical Observations and Instrumentation
