[Models] Invalid values in images and ground truths

[Gonzalo Mateo García]

Satellite images (input to the models) have missing values (in Sentinel-2 encoded as 0).
Ground truth mask (output to the models) could also have missing values (also encoded as the 0 class).

Currently ground truths are set to invalid when the input is invalid. worldfloods_internal/compute_meta_tiff.py

Options for training the model (how we should deal with invalids in the input/output the loss function):

• Option 1: replace invalid in output with land class (inpainting?)
• Option 2: learn a separate invalid class
• Option 3: Mask out invalids from loss
• Option 4: use uncertainty as a proxy to invalids ([.333, .333, .333])
• Option 5: use a model to inpaint the image (cloud or/and missing removal model)

If more than 1 option is implemented we can have a value in the config.yaml file that selects what's the invalid policy used for training.