Sometimes I'd like to fit a single model using superClass or unsuperClass on several disjointed areas where I have raster data of the same variables. The way I've done this is the past for supervised classification is to sample each raster at the training points to create a single dataframe, then fit the model in caret, and the use raster::predict on each raster individually. It would be nice however if this could be done directly in RSToolbox and perhaps with the upgrade to terra you could include SpatRasterDatasets as a supported input which is essentially like having a list of rasters/raster stacks that can be of differing extents.
Sometimes I'd like to fit a single model using
superClassorunsuperClasson several disjointed areas where I have raster data of the same variables. The way I've done this is the past for supervised classification is to sample each raster at the training points to create a single dataframe, then fit the model incaret, and the useraster::predicton each raster individually. It would be nice however if this could be done directly inRSToolboxand perhaps with the upgrade toterrayou could includeSpatRasterDatasetsas a supported input which is essentially like having alistof rasters/raster stacks that can be of differing extents.