Final remote sensing programing course presentation

Final remote sensing programing course presentation

April 19, 2018

The final project presentations of the spatial coding course by the EAGLE students revealed quite some impressive analysis achieved within the last couple of months. All analysis were done using R and presentations created within R using knitr. The aim was to run a variety of remote sensing analysis using R coding in order to increase the coding and remote sensing skills.

The project topics ranged from landcover change detection to sensitivity analysis of thresholds, covariates or training data concerning supervised classifications. All students added their scripts to a git and some even provided dedicated R packages for their specific analysis.

It was great to experience their coding skill development using remote sensing data over the past last months and to see their impressive achievements. Looking very much forward to the next courses.

follow us and share it on:

you may also like:

EAGLE Goes Bouldering: Getting to Know the New Cohort

EAGLE Goes Bouldering: Getting to Know the New Cohort

Some of the best conversations don’t happen in a seminar room but outside during a hike or - in this case - a climb. This week, the new EAGLE students got a chance to prove it, swapping lecture slides for climbing shoes at our bouldering social. We booked the Rock-In...

Teaching activities in the Arctic: EORC at UNIS on Svalbard

Teaching activities in the Arctic: EORC at UNIS on Svalbard

It started a few years ago, when Mirjana Bevanda built up a collaboration with partners at UNIS, the University Centre in Svalbard. Jakob Schwalb-Willmann joined soon after, and today a growing group from EORC heads north to teach there, including Antonio...

bzgl. Art. 50 Verordnung (EU) 2024/1689 (AI Act):

Überwiegend werden eigene originäre Texte und Bilder des EORC genutzt, jedoch sind einige Inhalte von blog post Texten teilweise mit Hilfe von KI überarbeitet worden und einige Bilder sind ganz mit KI erstellt, die jedoch deutlich keine photorealistische Darstellungen abbilden. Aussnahmen sind KI generierte fernerkundliche Datensätze, die explizit für Forschungszwecke mit KI erstellt wurden, hier wird aber durch den assoziierten Text der wissenschaftliche Grund der KI generierten Bilder erläutert.

Share This