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The Centre for Environmental Studies (CFES) at the Department of Geography, Geoinformatics and Meteorology invites you to attend the Seminar on “Development of earth observation data cubes for monitoring land degradation processes in South Africa”. The seminar will be presented by Dr Insa Otte from the University of Würzburg, Germany. Dr Otte will be presenting some results from the SALDi project focusing on mapping land degradation in 6 sites across South Africa. The South African Land Degradation
Monitor ( SALDi ) project was led by Dr Jussi Baade, from the University of Jena, Germany.
Join the GIS Applications for Water Engineers course scheduled for 11-13 October. The course provides attendees with an introduction to Geographical Information System (GIS) applications that are frequently used for planning and design of water engineering infrastructure. Through a combination of interactive presentations, hands-on exercises and group discussions, attendees will gain insight into the advantages of using GIS software for planning, design and risk management. The intention of the exercises is to guide attendees through a typical project, from data acquisition, all the way through to result presentation. The course presents a great opportunity for attendees who want to further their professional careers in the water engineering environment and related fields such as water infrastructure development planning, hydrological modelling and climate change adaptation planning.
The department of Geography, Geoinformatics and Meteorology will offer a Remote sensing data analysis in R-Studio: A machine learning perspective from 8-10 November 2022. This course will be led by Dr. Philemon Tsele and is targeting those working with big data within the remote sensing industry and how certain advanced algorithms such as artificial intelligence and machine learning are used within this context. This remote sensing data analysis course covers the physical principles of satellite remote sensing and remote sensor data processing using machine learning methods.
For more information please visit https://www.enterprises.up.ac.za/remote-sensing-data-analysis-in-r-studio-a-machine-learning-perspective-4.
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