{"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/exploiting-convnet-diversity-for-flooding","title":"Exploiting ConvNet Diversity for Flooding Identification","arxiv_id":"1711.03564","date":"2017-11-09","proceeding":null,"authors":["Keiller Nogueira","Samuel G. Fadel","Ícaro C. Dourado","Rafael de O. Werneck","Javier A. V. Muñoz","Otávio A. B. Penatti","Rodrigo T. Calumby","Lin Tzy Li","Jefersson A. dos Santos","Ricardo da S. Torres"],"abstract":"Flooding is the world's most costly type of natural disaster in terms of both\neconomic losses and human causalities. A first and essential procedure towards\nflood monitoring is based on identifying the area most vulnerable to flooding,\nwhich gives authorities relevant regions to focus. In this work, we propose\nseveral methods to perform flooding identification in high-resolution remote\nsensing images using deep learning. Specifically, some proposed techniques are\nbased upon unique networks, such as dilated and deconvolutional ones, while\nother was conceived to exploit diversity of distinct networks in order to\nextract the maximum performance of each classifier. Evaluation of the proposed\nalgorithms were conducted in a high-resolution remote sensing dataset. Results\nshow that the proposed algorithms outperformed several state-of-the-art\nbaselines, providing improvements ranging from 1 to 4% in terms of the Jaccard\nIndex.","url_abs":"http://arxiv.org/abs/1711.03564v2","url_pdf":"http://arxiv.org/pdf/1711.03564v2.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":"exploiting-convnet-diversity-for-flooding","repo_url":"https://github.com/keillernogueira/FDSI","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}