Raw features are in a gridded format (either GRIB or NetCDF) and are resampled to a common spatial resolution in the pre-processing step. After resampling, features are extracted at each point of interest. The machine learning models implemented use in input a table where features and outcomes are columns and each row correspond to a point (lon and lat coordinates).
Please note the resampling step introduces errors/loss of information and it's not necessarily needed in this case. Please consider inspecting each raw feature in its original spatial resolution, to minimise information loss.
This issue is solved in branch: cvitolo-patch-1.
Training script will need some modification to accept new inputs.
Will close this issue when the branch is merged into master.
Raw features are in a gridded format (either GRIB or NetCDF) and are resampled to a common spatial resolution in the pre-processing step. After resampling, features are extracted at each point of interest. The machine learning models implemented use in input a table where features and outcomes are columns and each row correspond to a point (lon and lat coordinates).
Please note the resampling step introduces errors/loss of information and it's not necessarily needed in this case. Please consider inspecting each raw feature in its original spatial resolution, to minimise information loss.