{"id":551,"date":"2023-04-27T15:31:17","date_gmt":"2023-04-27T14:31:17","guid":{"rendered":"https:\/\/blogg.lnu.se\/disa\/?p=551"},"modified":"2023-05-04T15:33:13","modified_gmt":"2023-05-04T14:33:13","slug":"workshop-may-4th-2023-on-self-supervised-deep-learning-in-eo-based-forest-inventory-esa-represent-project-and-forest-thematic-exploitation-platform-f-tep","status":"publish","type":"post","link":"https:\/\/blogg.lnu.se\/disa\/?p=551","title":{"rendered":"Workshop (May 4th, 2023) on Self-supervised deep learning in EO-based forest inventory – ESA RepreSent project and Forest Thematic Exploitation Platform (F-TEP)"},"content":{"rendered":"

Deep learning (DL) and computer vision are rapidly gaining popularity in forest inventory. However, the scarcity of available reference data limits the effective use of DL tools. Self-supervised learning (SSL) and weakly-supervised learning aim to solve this bottleneck by enabling better utilization of available EO data to effectively train DL models.<\/p>\n

We invite you to attend an online workshop, where we will present and discuss several deep learning models suitable for forest mapping with satellite remote sensing data<\/em><\/strong>, that were created within the ESA funded RepreSent<\/a> project (2022-2023) on Representation learning for Copernicus Sentinel data. The developed models enable forest mapping and monitoring by significantly reducing the amount of reference data typically required for deep learning model training. A selected set of tools has also been implemented on Forestry TEP to facilitate the quick adoption of developed methodologies in the downstream sector and for potential use as benchmark methodologies.<\/p>\n

The workshop targets AI4EO researchers who are interested in the forestry sector<\/em><\/strong>, as well as foresters who wish to explore the broader applications of DL and SSL in their academic research or operational forest management<\/em><\/strong>.<\/p>\n

Please inform about your participation<\/strong> using this link https:\/\/forms.office.com\/e\/GFKbeZ29jQ<\/a><\/p>\n

Participation to the workshop is free.<\/p>\n

The online workshop will be organized on MS Teams, and further details will be sent to registered participants.<\/p>\n

Preliminary agenda – the online workshop starts at 10 am EEST (Finland time zone), May 4th<\/sup> 2023<\/u><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
10:00 am (EEST)<\/strong><\/td>\nWelcome and ESA RepreSent project introduction<\/td>\nMatthieu Molinier<\/strong>, Oleg Antropov, VTT, Corneliu Octavian Dumitru, DLR<\/td>\n<\/tr>\n
10:05<\/strong><\/td>\nForest inventory using EO data<\/td>\nJukka Miettinen<\/strong>, Tuomas H\u00e4me, VTT<\/td>\n<\/tr>\n
10:15<\/strong><\/td>\nSelf-supervised and weakly-supervised Learning in Earth Observation<\/td>\nESA Represent consortium<\/td>\n<\/tr>\n
10:25<\/strong><\/p>\n

10min + 5min Q&A<\/td>\n

MoCo & MAML models in forest mapping using Copernicus Sentinel-2 and Sentinel-1 data<\/td>\nLloyd Hughes<\/strong>, Marc Russwurm,<\/p>\n

Devis Tuia, EPFL<\/td>\n<\/tr>\n

10:40<\/strong><\/p>\n

10min +5 min Q&A<\/td>\n

UNet+ models with multi-source EO data<\/td>\nOleg Antropov<\/strong>, VTT<\/td>\n<\/tr>\n
10:55<\/strong><\/p>\n

10 min +5 min Q&A<\/td>\n

DCVA approaches for forest change detection using Sentinel-2 images<\/td>\nRidvan Kuzu<\/strong>, DLR<\/td>\n<\/tr>\n
11:10<\/strong><\/td>\nBreak (5 mn)<\/td>\n<\/tr>\n
11:15<\/strong><\/p>\n

20 min +5 min Q&A<\/td>\n

F-TEP introduction: Status and tools overview<\/td>\nJukka Miettinen<\/strong>, Renne Tergujeff, VTT<\/td>\n<\/tr>\n
11:40<\/strong><\/p>\n

20 min + 5 min Q&A<\/td>\n

F-TEP service demonstrations including SSL<\/td>\nLauri Seitsonen<\/strong>, VTT<\/td>\n<\/tr>\n
12:05<\/strong><\/td>\nF-TEP developer\u2019s perspective<\/td>\nLauri Seitsonen,<\/strong> VTT<\/td>\n<\/tr>\n
12:15<\/strong><\/td>\nConcluding remarks<\/td>\nOleg Antropov<\/strong>, Matthieu Molinier, VTT<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Please forward this invitation to your colleagues who might be interested in these topics.<\/em><\/p>\n

We look forward to meeting you at the workshop.<\/p>\n

Oleg Antropov, Matthieu Molinier, VTT and the ESA RepreSent team<\/p>\n","protected":false},"excerpt":{"rendered":"

Deep learning (DL) and computer vision are rapidly gaining popularity in forest inventory. However, the scarcity of available reference data limits the effective use of DL tools. Self-supervised learning (SSL) and weakly-supervised learning aim to solve this bottleneck by enabling better utilization of available EO data to effectively train DL models. We invite you to […]<\/p>\n","protected":false},"author":401,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[459,27498],"tags":[],"class_list":["post-551","post","type-post","status-publish","format-standard","hentry","category-events","category-forestry"],"acf":[],"yoast_head":"\r\nWorkshop (May 4th, 2023) on Self-supervised deep learning in EO-based forest inventory - ESA RepreSent project and Forest Thematic Exploitation Platform (F-TEP) - DISA<\/title>\r\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\r\n<link rel=\"canonical\" href=\"https:\/\/blogg.lnu.se\/disa\/?p=551\" \/>\r\n<meta property=\"og:locale\" content=\"en_US\" \/>\r\n<meta property=\"og:type\" content=\"article\" \/>\r\n<meta property=\"og:title\" content=\"Workshop (May 4th, 2023) on Self-supervised deep learning in EO-based forest inventory - ESA RepreSent project and Forest Thematic Exploitation Platform (F-TEP) - DISA\" \/>\r\n<meta property=\"og:description\" content=\"Deep learning (DL) and computer vision are rapidly gaining popularity in forest inventory. 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