{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cloudsegnet-a-deep-network-for-nychthemeron","title":"CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation","arxiv_id":"1904.07979","date":"2019-04-16","proceeding":null,"authors":["Soumyabrata Dev","Atul Nautiyal","Yee Hui Lee","Stefan Winkler"],"abstract":"We analyze clouds in the earth's atmosphere using ground-based sky cameras.\nAn accurate segmentation of clouds in the captured sky/cloud image is\ndifficult, owing to the fuzzy boundaries of clouds. Several techniques have\nbeen proposed that use color as the discriminatory feature for cloud detection.\nIn the existing literature, however, analysis of daytime and nighttime images\nis considered separately, mainly because of differences in image\ncharacteristics and applications. In this paper, we propose a light-weight\ndeep-learning architecture called CloudSegNet. It is the first that integrates\ndaytime and nighttime (also known as nychthemeron) image segmentation in a\nsingle framework, and achieves state-of-the-art results on public databases.","url_abs":"http://arxiv.org/abs/1904.07979v1","url_pdf":"http://arxiv.org/pdf/1904.07979v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"cloudsegnet-a-deep-network-for-nychthemeron","repo_url":"https://github.com/Soumyabrata/CloudSegNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"cloud-detection","task_name":"Cloud Detection"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[{"slug":"swinyseg","name":"SWINySEG","full_name":"Singapore Whole sky Nychthemeron Image SEGmentation Database"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}