{"paper_id":"0241956b-ec62-4afd-be0a-b1e85e0ada55","body_text":"Deep learning–based semantic segmentation of night-sky clouds for operational telescope scheduling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Deep learning–based semantic segmentation of night-sky clouds for operational telescope scheduling Xuan Liu, Hai Cao, Ruojun Wang, Shaoming Hu, DiFu Guo, Xu Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7856026/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Ground-based optical telescopes necessitate prompt and spatially detailed information regarding dome-scale cloud coverage to facilitate target-specific shuttering and scheduling decisions. When only coarse or delayed atmospheric data are available, observatories risk inefficient use of scarce dark time and the irreversible loss of scientific exposures. To address this, we introduce WOANC dataset, a pixel-annotated nighttime full-dome dataset acquired at an operational observatory, alongside NightCloudSegNet, a fisheye-aware segmentation framework specifically designed for low-light astronomical imaging. Evaluated on the WOANC test set, NightCloudSegNet achieves a mean intersection-over-union (mIoU) of 86.6% and a pixel-level F1 score of 92.8%. Furthermore, when tested on the external SWINSEG dataset, the model attains an mIoU of 86.2% and an F1 score of 92.6%, thereby demonstrating robust performance under conditions of fisheye distortion and low illumination. By translating pixel-level segmentation masks into per-target observability indicators, this approach enables informed shuttering and scheduling decisions that significantly reduce unnecessary telescope operations and enhance observational efficiency. Physical sciences/Mathematics and computing Physical sciences/Optics and photonics Nighttime cloud detection All-sky fisheye imaging Telescope scheduling Deep-learning semantic segmentation Observability assessment Astronomical automation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Feb, 2026 Reviews received at journal 09 Feb, 2026 Reviews received at journal 08 Feb, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers invited by journal 03 Nov, 2025 Editor invited by journal 20 Oct, 2025 Editor assigned by journal 16 Oct, 2025 Submission checks completed at journal 16 Oct, 2025 First submitted to journal 14 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7856026\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":532346816,\"identity\":\"73599b9d-bbb2-490e-8abc-af52eb58de24\",\"order_by\":0,\"name\":\"Xuan Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shandong University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xuan\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":532346817,\"identity\":\"590791c6-8fda-43ce-87bd-cc45c89d492f\",\"order_by\":1,\"name\":\"Hai 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