Check proposal status of processes

[m-mohr]

A process can change to stable status with the following requirements:

Processes will only be moved from proposals to the stable process specifications once there are at least two implementations and an example process in the examples folder showing it in a use case. This doesn't require a PSC vote individually as it's not a breaking change, just an addition.

I need to check how many implementations are available and check for (and add) examples.

Related to: https://github.com/openEOPlatform/architecture-docs/issues/21#issuecomment-969134299, where we may want to get the following processes stable:

• array_concat
• array_create

Debatable due to missing implementations as of now:

• array_modify
• nan
• is_infinite

[m-mohr]

None of them qualifies right now.

[clausmichele]

I will check with EODC if we can port there aggregate_spatial_window. In that case it would be available at Eurac and EODC.

[m-mohr]

Thanks @clausmichele. EODC and EURAC would share the same underlying code, right? I'm trying to get to two distinct implementations (i.e. different code/libraries used) so that we can ensure it's somewhat possible to implement in different environments. Sharing (mostly) the same underlying implementation and exposing at two back-ends should not necessarily qualify as two implementations. :-)

[m-mohr]

The following processes would qualify to get stable (ignoring the outdated "examples" requirement):

• array_append (S, V)
• array_concat (EO, S, V)
• array_interpolate_linear (EU, S, V)
• array_create (EO, S, V)
• resample_cube_temporal (EU, SH)

EO = EODC, EU = EURAC, V = VITO, S = SentinelHub

As such I'd propose to make the 5 processes above stable in 2.0.0.

These also apply, but are still somewhat experimental due to varying implementations or open issues:

• atmospheric_correction
• ard_normalized_radar_backscatter
• load_result
• sar_backscatter

These also apply, but are expected to change due to improvements to general ML handling #396:

• fit_class_random_forest
• fit_regr_random_forest
• fit_curve
• load_ml_model
• predict_curve
• predict_random_forest
• save_ml_model