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It must be clear when, where and how the data is brought to the same grid. The simplest approach is to resample everything before it is passed to the inference engine. When data from different sources is combined though, the inference engine might want to decide on how this combination is done not only spectrally/thematically, but also spatially.
If the resampling is done outside of the inference engine, we might consider adding a new component for this.
The text was updated successfully, but these errors were encountered:
The input observations, observations mask & associated uncertainties need to be resampled to match the state mask. For S2 (and similarly for S1), this is done here. The actual function that does the resampling could be hosted elsewhere (I just never figure out where).
I have already put that function into multiply-core. That is the place where we should put shared code.
When you say that you resample it to the state mask, is that because you assume the state mask has the spatial extent and resolution specified by the user? I am asking because it might happen that we have to resample the state mask, too, doesn't it? (if it is provided as vector data, we first have to bring it to a grid, so we can take the requested one right away, of course)
It must be clear when, where and how the data is brought to the same grid. The simplest approach is to resample everything before it is passed to the inference engine. When data from different sources is combined though, the inference engine might want to decide on how this combination is done not only spectrally/thematically, but also spatially.
If the resampling is done outside of the inference engine, we might consider adding a new component for this.
The text was updated successfully, but these errors were encountered: