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E-TRAINEE project: E-learning course on Time Series Analysis in Remote Sensing

Oct 10th, 2020 by Katharina Anders

The E-TRAINEE project is a new collaboration project for developing an “E-learning course on Time Series Analysis in Remote Sensing for Understanding Human-Environment Interactions” with Markéta Potůčková (Department of Applied Geoinformatics and Cartography, Charles University Prague) as PI of the project and Heidelberg University, University of Innsbruck and University of Warsaw as project partners. The E-TRAINEE project is funded in the framework of the Erasmus+ programme of the European Union.

The project’s objective is to develop a comprehensive research-oriented open e-learning course on time series analysis in remote sensing for environmental monitoring. The course offers a multidisciplinary approach connecting themes from computer science, geography, and environmental studies.

It combines well-established and latest technologies of remote sensing (satellite and UAV sensing, multispectral and hyperspectral sensing, 3D point clouds) and methods of artificial intelligence (machine and deep learning) in order to use these technological developments to understand environmental changes and interaction of human activities and environment. It shows how the same environmental phenomenon can be analysed from the perspective of different data sources, scales and time frequencies.

The 3DGeo research group supports this project with contents on 3D/4D geospatial point clouds and methods for their analysis, including machine learning, time series analysis, and laser scanning simulation. Contents will further comprise programming for point cloud analysis in Python and research-oriented case studies.

We are looking forward to bring our 4D research into international education!

The collaboration project follows the alliance built up through the 4EU+ collaboration project “3D Landcover Monitoring”.

Tags: 3D Landcover Monitoring, 3DGEO, 4D, e-learning, E-TRAINEE, geospatial data, HELIOS, Lidar, machine leanring, Multisource and multitemporal data fusion, python, remote sensing, time series analysis

Posted in 3D, Lidar Group, Research, Teaching

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