Package provides easier access to EU environmental maps and functions to produce and improve new value-added spatial layers. Key functionality includes:
*`train.spm` --- train a spatial prediction model using [mlr3 package](https://mlr3.mlr-org.com/)) implementation with spatial coordinates and spatial cross-validation,
*`accuracy.plot` --- plots predicted vs observed values based on the result of `train.spm`,
*`extract.tif` --- extracts points with space-time observations from a list of tifs with different begin / end reference periods,
*`predict.spm` --- can be used to predict values from a fitted spatial prediction model at new locations,
A general tutorial for `train.spm` is available [here](https://gitlab.com/geoharmonizer_inea/eumap/-/tree/master/demo/spm-tutorial). The eumap package builds up on top of the [mlr3](https://mlr3.mlr-org.com/), [terra](https://github.com/rspatial/terra) and similar packages. As such, it's main purpose is to automate as much as possible Machine Learning and prediction in a scalable system.
In the Geo-harmonizer project, we prepare Analysis-Ready Earth Observation images from Landsat and
Sentinel missions, then use ground observations from the European Commission projects such as [LUCAS surveys](https://land.copernicus.eu/imagery-in-situ/lucas) and CORINE and similar to
overlay the ground observations in the spacetime cubes. From this data we create spatiotemporal regression and classification matrices (see: [sample data set](https://doi.org/10.5281/zenodo.4058447)). The eumap package (python and R versions) will allow accessing this data and testing models that apply Machine Learning for predictive mapping in spacetime.
