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darts_preprocessing.preprocess_legacy_fast

Preprocess optical data with legacy (DARTS v1) preprocessing steps, but with new data concepts.

The processing steps are: - Calculate NDVI - Calculate slope and relative elevation from ArcticDEM - Merge everything into a single ds.

The main difference to preprocess_legacy is the new data concept of the arcticdem. Instead of using already preprocessed arcticdem data which are loaded from a VRT, this step expects the raw arcticdem data and calculates slope and relative elevation on the fly.

Parameters:

  • ds_merged (xarray.Dataset) –

    The Planet scene optical data or Sentinel 2 scene optical dataset including data_masks.

  • ds_arcticdem (xarray.Dataset) –

    The ArcticDEM dataset.

  • ds_tcvis (xarray.Dataset) –

    The TCVIS dataset.

  • tpi_outer_radius (int, default: 100 ) –

    The outer radius of the annulus kernel for the tpi calculation in m. Defaults to 100m.

  • tpi_inner_radius (int, default: 0 ) –

    The inner radius of the annulus kernel for the tpi calculation in m. Defaults to 0.

  • device (typing.Literal['cuda', 'cpu'] | int, default: darts_preprocessing.preprocess.DEFAULT_DEVICE ) –

    The device to run the tpi and slope calculations on. If "cuda" take the first device (0), if int take the specified device. Defaults to "cuda" if cuda is available, else "cpu".

Returns:

Source code in darts-preprocessing/src/darts_preprocessing/preprocess.py 
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@stopuhr.funkuhr("Preprocessing", printer=logger.debug)
def preprocess_legacy_fast(
    ds_merged: xr.Dataset,
    ds_arcticdem: xr.Dataset,
    ds_tcvis: xr.Dataset,
    tpi_outer_radius: int = 100,
    tpi_inner_radius: int = 0,
    device: Literal["cuda", "cpu"] | int = DEFAULT_DEVICE,
) -> xr.Dataset:
    """Preprocess optical data with legacy (DARTS v1) preprocessing steps, but with new data concepts.

    The processing steps are:
    - Calculate NDVI
    - Calculate slope and relative elevation from ArcticDEM
    - Merge everything into a single ds.

    The main difference to preprocess_legacy is the new data concept of the arcticdem.
    Instead of using already preprocessed arcticdem data which are loaded from a VRT, this step expects the raw
    arcticdem data and calculates slope and relative elevation on the fly.

    Args:
        ds_merged (xr.Dataset): The Planet scene optical data or Sentinel 2 scene optical dataset including data_masks.
        ds_arcticdem (xr.Dataset): The ArcticDEM dataset.
        ds_tcvis (xr.Dataset): The TCVIS dataset.
        tpi_outer_radius (int, optional): The outer radius of the annulus kernel for the tpi calculation
            in m. Defaults to 100m.
        tpi_inner_radius (int, optional): The inner radius of the annulus kernel for the tpi calculation
            in m. Defaults to 0.
        device (Literal["cuda", "cpu"] | int, optional): The device to run the tpi and slope calculations on.
            If "cuda" take the first device (0), if int take the specified device.
            Defaults to "cuda" if cuda is available, else "cpu".

    Returns:
        xr.Dataset: The preprocessed dataset.

    """
    # Calculate NDVI
    ds_merged["ndvi"] = calculate_ndvi(ds_merged).ndvi

    # Reproject TCVIS to optical data
    with stopuhr.stopuhr("Reprojecting TCVIS", printer=logger.debug):
        ds_tcvis = ds_tcvis.odc.reproject(ds_merged.odc.geobox, resampling="cubic")

    ds_merged["tc_brightness"] = ds_tcvis.tc_brightness
    ds_merged["tc_greenness"] = ds_tcvis.tc_greenness
    ds_merged["tc_wetness"] = ds_tcvis.tc_wetness

    # Calculate TPI and slope from ArcticDEM
    with stopuhr.stopuhr("Reprojecting ArcticDEM", printer=logger.debug):
        ds_arcticdem = ds_arcticdem.odc.reproject(ds_merged.odc.geobox.buffered(tpi_outer_radius), resampling="cubic")

    ds_arcticdem = preprocess_legacy_arcticdem_fast(ds_arcticdem, tpi_outer_radius, tpi_inner_radius, device)
    ds_arcticdem = ds_arcticdem.odc.crop(ds_merged.odc.geobox.extent)
    # For some reason, we need to reindex, because the reproject + crop of the arcticdem sometimes results
    # in floating point errors. These error are at the order of 1e-10, hence, way below millimeter precision.
    ds_arcticdem = ds_arcticdem.reindex_like(ds_merged)

    ds_merged["dem"] = ds_arcticdem.dem
    ds_merged["relative_elevation"] = ds_arcticdem.tpi
    ds_merged["slope"] = ds_arcticdem.slope
    ds_merged["arcticdem_data_mask"] = ds_arcticdem.datamask

    # Update datamask with arcticdem mask
    # with xr.set_options(keep_attrs=True):
    #     ds_merged["quality_data_mask"] = ds_merged.quality_data_mask * ds_arcticdem.datamask
    # ds_merged.quality_data_mask.attrs["data_source"] += " + ArcticDEM"

    return ds_merged