{"id":151735,"date":"2023-05-25T15:11:06","date_gmt":"2023-05-25T13:11:06","guid":{"rendered":"https:\/\/uniavisen.dk\/event\/diku-bits-deep-learning-and-remote-sensing-for-ecosystem-monitoring\/"},"modified":"2023-05-25T15:11:06","modified_gmt":"2023-05-25T13:11:06","slug":"diku-bits-deep-learning-and-remote-sensing-for-ecosystem-monitoring","status":"publish","type":"event","link":"https:\/\/uniavisen.dk\/en\/event\/diku-bits-deep-learning-and-remote-sensing-for-ecosystem-monitoring\/","title":{"rendered":"DIKU Bits: Deep learning and remote sensing for ecosystem monitoring"},"content":{"rendered":"<h2>Speaker<\/h2>\n<p><a href=\"\/fa-sites\/forsk2\/soeg\/result\/?pure=da%2Fpersons%2F400547\">Christian Igel<\/a>,\u00a0Professor in the Machine Learning\u00a0section\u00a0(ML)<\/p>\n<h2>Title<\/h2>\n<p>Deep learning and remote sensing for ecosystem monitoring<\/p>\n<h2><strong>Abstract<\/strong><\/h2>\n<p>Deep learning applied to remote sensing data allows for large-scale mapping of individual trees and forests, which enables more accurate estimation of captured carbon. This talk demonstrates tree crown segmentation in satellite images using fully convolutional neural networks (CNNs) and how allometric equations can then be applied to estimate biomass. Then it is shown how CNNs for 3D point clouds can estimate tree biomass directly based on LiDAR (laser imaging, detection, and ranging).<\/p>\n<h3><strong>Which courses do you teach?<\/strong><\/h3>\n<p>Machine Learning A (MLA)<br \/>\nMachine Learning B (MLB)<br \/>\nOnline and Reinforcement Learning (OReL)<\/p>\n<h3><strong>Which technology\/research\/projects\/startup are you excited to see the evolution of?<\/strong><\/h3>\n<p>I am just in the process of co-founding a startup in the domain of medical data analysis, and I am very excited about it.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Speaker Christian Igel,\u00a0Professor in the Machine Learning\u00a0section\u00a0(ML) Title Deep learning and remote sensing for ecosystem monitoring Abstract Deep learning applied to remote sensing data allows for large-scale mapping of individual trees and forests, which enables more accurate estimation of captured carbon. This talk demonstrates tree crown segmentation in satellite images using fully convolutional neural networks [&hellip;]<\/p>\n","protected":false},"author":0,"featured_media":151736,"template":"","class_list":["post-151735","event","type-event","status-publish","has-post-thumbnail","hentry","event_category-forelaesning"],"acf":[],"aioseo_notices":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>DIKU Bits: Deep learning and remote sensing for ecosystem monitoring<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/uniavisen.dk\/event\/diku-bits-deep-learning-and-remote-sensing-for-ecosystem-monitoring\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"DIKU Bits: Deep learning and remote sensing for ecosystem monitoring\" \/>\n<meta property=\"og:description\" content=\"Speaker Christian Igel,\u00a0Professor in the Machine Learning\u00a0section\u00a0(ML) Title Deep learning and remote sensing for ecosystem monitoring Abstract Deep learning applied to remote sensing data allows for large-scale mapping of individual trees and forests, which enables more accurate estimation of captured carbon. 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