BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Kolkata BEGIN:STANDARD DTSTART:19451014T230000 TZOFFSETFROM:+0630 TZOFFSETTO:+0530 TZNAME:IST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20240408T121240Z UID:07330C6A-4369-49C0-B848-A9FDCA2A61AB DTSTART;TZID=Asia/Kolkata:20240316T140000 DTEND;TZID=Asia/Kolkata:20240316T153000 DESCRIPTION:Deep learning (DL)\, a branch of machine learning (ML) and arti ficial intelligence (AI) is nowadays considered as a core technology of to day’s Fourth and Fifth Industrial Revolution (Industry 4.0 and Industry 5.0). Due to its learning capabilities from raw data\, DL technology origi nated from artificial neural network (ANN)\, has become a hot topic in the context of computing\, and is widely applied in various application resea rch areas like healthcare\, remote sensing\, semiconductor manufacturing i ndustry\, visual recognition\, natural language processing\, text analytic s\, cybersecurity\, 5G Networks and many more. However\, building an appro priate DL model is a challenging task\, due to the dynamic nature and vari ations in real-world problems and data. Moreover\, the lack of core unders tanding turns DL methods into black-box machines that hamper development a t the standard level for societal applications. This session presents DL a lgorithms for Land Cover Mapping using Python Programming and deep Learnin g TensorFlow Library. This session also summarizes real-world application areas where deep learning techniques can be used. Finally\, Practical demo nstration of UNet and DPPNet model for Land Cover Mapping using Satellite Images and Python Deep Learning TensorFlow Library.\n\nLearning Objectives :\nThe primary objective of the expert talk is to enlighten the audience o n the potential and challenges of using Deep Learning Algorithms for Land Cover Mapping from Satellite Images both theoretical and practical. At the end of the session\, the participants should be able to understand:\n\n- Understand fundamental concepts of 2D U-Net and DPPNet CNN Models\n- The c hallenges of working with Land Cover Mapping from Satellite Image\n- Under stand the advance deep learning algorithms\n- How to practically implement DL techniques using TensorFlow\n\nTarget Audience: Students\, researchers \, academic and industry professionals who have foundational theoretical k nowledge of Probability\, Linear Algebra\, Computer Vision\, Image Process ing and Fundamental of Artificial Neural Networks.\n\nSpeaker(s): Dr. Shya m Lal\n\nBldg: Kengeri Campus\, CHRIST (Deemed to be University)\, \, Bang alore\, Karnataka\, India\, 560074 LOCATION:Bldg: Kengeri Campus\, CHRIST (Deemed to be University)\, \, Banga lore\, Karnataka\, India\, 560074 ORGANIZER:ieee.cs@christuniversity.in SEQUENCE:3 SUMMARY:Computing Algorithm for Geospatial Data Session 2 URL;VALUE=URI:https://events.vtools.ieee.org/m/409682 X-ALT-DESC:Description: <br /><p>Deep learning (DL)\, a branch of machine l earning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today&rsquo\;s Fourth and Fifth Industrial Revolution ( Industry 4.0 and Industry 5.0). Due to its learning capabilities from raw data\, DL technology originated from artificial neural network (ANN)\, has become a hot topic in the context of computing\, and is widely applied in various application research areas like healthcare\, remote sensing\, sem iconductor manufacturing industry\, visual recognition\, natural language processing\, text analytics\, cybersecurity\, 5G Networks and many more. H owever\, building an appropriate DL model is a challenging task\, due to t he dynamic nature and variations in real-world problems and data. Moreover \, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level for societal applications. This session presents DL algorithms for Land Cover Mapping using Python Pr ogramming and deep Learning TensorFlow Library. This session also summariz es real-world application areas where deep learning techniques can be used . Finally\, Practical demonstration of UNet and DPPNet model for Land Cove r Mapping using Satellite Images and Python Deep Learning TensorFlow Libra ry.<br><br></p>\n<p><strong>Learning Objectives:</strong><br>The primary o bjective of the expert talk is to enlighten the audience on the potential and challenges of using Deep Learning Algorithms for Land Cover Mapping fr om Satellite Images both theoretical and practical. At the end of the sess ion\, the participants should be able to understand:</p>\n<ul>\n<li>Unders tand fundamental concepts of 2D U-Net and DPPNet CNN Models</li>\n<li>The challenges of working with Land Cover Mapping from Satellite Image</li>\n< li>Understand the advance deep learning algorithms</li>\n<li>How to practi cally implement DL techniques using TensorFlow</li>\n</ul>\n<p><strong>Tar get Audience:</strong> Students\, researchers\, academic and industry prof essionals who have foundational theoretical knowledge of Probability\, Lin ear Algebra\, Computer Vision\, Image Processing and Fundamental of Artifi cial Neural Networks.</p> END:VEVENT END:VCALENDAR