2021-09-06, 13:30–15:00, HUGOTech
Software requirements: opengeohub/r-geo docker image (R, rgdal, terra, mlr3), QGIS, Google Earth Pro
Content:
Introduction to Ensemble Machine Learning: the mlr3 framework,
Selecting learners, fine-tuning, feature selection and model stacking,
Using Machine Learning with spatial and spatiotemporal data:
Using ML for spatial interpolation: landmap package (vs geoR and similar geostatistical software),
Adding geographical distances and features to spatial interpolation,
Fitting and using EML for predicting eumap land cover data (Witjes et al, 2021),
Technical director at OpenGeoHub Foundation
- Spatiotemporal modeling of environmental dynamics at global scale: building open multiscale data cubes
- Opening plenary
- Awards and closing plenary
- Introduction to spatial and spatiotemporal data in R
- Computing with Cloud-Optimized GeoTIFFs in R
- Data visualization: from R to Google Earth and QGIS
- Modeling with spatial and spatiatemporal data in R