Training in the use of machine learning techniques in remote sensing: Part 1
Welcome to the first training session about machine learning techniques in remote sensing. The training is organized by the RESDINET project (Network for novel remote sensing technologies in forest disturbance ecology), funded by the European Union.
Don’t forget to register for the second part of the workshop, organized on Thursday 15 February 9:00–12:15.
Program
9:00–9:10 Eija Honkavaara, Finnish Geospatial Research Institute, “Introduction to remote sensing of bark beetle disturbance training event”
9:10–10:00 Ilkka Pölönen, University of Jyväskylä, “Machine learning for vegetation health monitoring using hyperspectral remote sensing data” (online)
10:00–10:15 Coffee break
10:15–11:15 Training ML models, Raquel Alves de Oliveira & Roope Näsi, Finnish Geospatial Research Institute
- Hands on experiments
- Homework (RGB, multispectral, hyperspectral)
11:15–12:15 Zbynek Malenevsky, University of Bonn, “High spatial resolution imaging spectroscopy methods for monitoring vegetation stress” (online)
Speakers
Eija Honkavaara – Research Professor, Finnish Geospatial Research Institute FGI, National Land Survey of Finland
Ilkka Pölönen – Associate Professor of Computational Data Analysis, University of Jyväskylä
Raquel Alves de Oliveira – Senior Research Scientist, Finnish Geospatial Research Institute FGI, National Land Survey of Finland
Roope Näsi – Senior Research Scientist, Finnish Geospatial Research Institute FGI, National Land Survey of Finland
Zbynek Malenevsky – Chair Professorship at Remote Sensing Research Group, University of Bonn
Detaljer
Datum
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Plats
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