Deployment of a multi-classifier approach to improve land cover classification accuracy

multi_classifierThis study will examine whether the application of hybrid classifiers increases the classification accuracy in comparison to a single classifier. A combination between parametric and non-parametric classifiers will be applied and their performance will be assessed. The student is expected to gain a deep knowledge of applied Machine Learning algorithms within this innovation laboratory.

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Our EAGLE Clara is doing her internship in the Arctic

Our EAGLE Clara is doing her internship in the Arctic

by | Sep 13, 2024 |

Clara, an 8th gen EAGLE, is currently doing her internship in the Arctic. She spends 2 months in Longyearbyen on Svalbard where she works with colleagues from UNIS who are already collaborating with our EORC scientists, namely Dr. Bevanda. Clara works at the Arctic...

Master Defense: Comparing the suitability of remote sensing and wildlife camera time series for deriving phenological metrics of understory vegetation in temperate forests of Upper Franconia, Bavaria

Master Defense: Comparing the suitability of remote sensing and wildlife camera time series for deriving phenological metrics of understory vegetation in temperate forests of Upper Franconia, Bavaria

by | Sep 12, 2024 |

On September 18, Sarah Schneider will present her master thesis "Comparing the suitability of remote sensing and wildlife camera time series for deriving phenological metrics of understory vegetation in temperate forests of Upper Franconia, Bavaria" at 14:00 in...

Cooperation with Namib desert Research Institute Gobabeb

Cooperation with Namib desert Research Institute Gobabeb

by | Sep 2, 2024 |

The Namib desert research station Gobabeb (www.gobabeb.org) is involved in diverse research projects and is hosting various students and researchers from a wide range of backgrounds and nationalities. They are also already involved in remote sensing and very...