{"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/land-cover-classification-from-remote-sensing","title":"Land Cover Classification from Remote Sensing Images Based on Multi-Scale Fully Convolutional Network","arxiv_id":"2008.00168","date":"2020-08-01","proceeding":null,"authors":["Rui Li","Shunyi Zheng","Chenxi Duan","Ce Zhang"],"abstract":"In this paper, a Multi-Scale Fully Convolutional Network (MSFCN) with multi-scale convolutional kernel is proposed to exploit discriminative representations from two-dimensional (2D) satellite images.","url_abs":"https://arxiv.org/abs/2008.00168v2","url_pdf":"https://arxiv.org/pdf/2008.00168v2.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":"land-cover-classification-from-remote-sensing","repo_url":"https://github.com/lironui/Multi-Scale-Fully-Convolutional-Network","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"land-cover-classification","task_name":"Land Cover Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}