{"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/deep-learning-for-regular-change-detection-in","title":"Deep Learning for Regular Change Detection in Ukrainian Forest Ecosystem With Sentinel-2","arxiv_id":null,"date":"2020-10-27","proceeding":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Volume: 14) 2020 10","authors":["Kostiantyn Isaienkov","Mykhailo Yushchuk","Vladyslav Khramtsov","Oleg Seliverstov"],"abstract":"The logging is the leading cause for the reduction in the forest area in the world. At the same time, the number of forest clear-cuts continues to grow. However, despite the massive scale, such incidents are difficult to track in time. As a result, huge areas of forests are gradually being cut down. Therefore, there is a need for regular and effective monitoring of changes in forest cover. The multi-temporal data sources like Copernicus Sentinel-2 allow enhancing the potential of monitoring the Earth’s surface and environmental dynamics including forest plantations. In this article, we present a baseline U-Net model for deforestation detection in the forest-steppe zone. Training and evaluation are conducted on our own data-set created on Sentinel-2 imagery for the Kharkiv region of Ukraine (31 400 km2). As a part of the research, we present several models with the ability to work with time-dependent imagery. The main contribution of this article is to provide a baseline model for the forest change detection inside Ukraine and improve it adding the ability to use several sequential images as an input of the segmentation model.","url_abs":"https://ieeexplore.ieee.org/abstract/document/9241044","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9241044","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":"deep-learning-for-regular-change-detection-in","repo_url":"https://github.com/QuantuMobileSoftware/forest_change_detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"change-detection","task_name":"Change Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}