Preprocessing Reference

darts_preprocessing

Data preprocessing and feature engineering for the DARTS dataset.

__version__ = importlib.metadata.version('darts-nextgen') module-attribute

preprocess_legacy(ds_optical, ds_arcticdem, ds_tcvis)

Preprocess optical data with legacy (DARTS v1) preprocessing steps.

The processing steps are: - Calculate NDVI - Merge everything into a single ds.

Parameters:

Name	Type	Description	Default
ds_optical	Dataset	The Planet scene optical data or Sentinel 2 scene optical data.	required
ds_arcticdem	Dataset	The ArcticDEM data.	required
ds_tcvis	Dataset	The TCVIS data.	required

Returns:

Type	Description
Dataset	xr.Dataset: The preprocessed dataset.

Source code in darts-preprocessing/src/darts_preprocessing/preprocess.py

def preprocess_legacy(
    ds_optical: xr.Dataset,
    ds_arcticdem: xr.Dataset,
    ds_tcvis: xr.Dataset,
) -> xr.Dataset:
    """Preprocess optical data with legacy (DARTS v1) preprocessing steps.

    The processing steps are:
    - Calculate NDVI
    - Merge everything into a single ds.

    Args:
        ds_optical (xr.Dataset): The Planet scene optical data or Sentinel 2 scene optical data.
        ds_arcticdem (xr.Dataset): The ArcticDEM data.
        ds_tcvis (xr.Dataset): The TCVIS data.

    Returns:
        xr.Dataset: The preprocessed dataset.

    """
    # Calculate NDVI
    ds_ndvi = calculate_ndvi(ds_optical)

    # Reproject TCVIS to optical data
    ds_tcvis = ds_tcvis.odc.reproject(ds_optical.odc.geobox, resampling="cubic")

    # Since this function expects the arcticdem to be loaded from a VRT, which already contains slope and tpi,
    # we dont need to calculate them here

    # merge to final dataset
    ds_merged = xr.merge([ds_optical, ds_ndvi, ds_arcticdem, ds_tcvis])

    return ds_merged

preprocess_legacy_fast(ds_merged, ds_arcticdem, ds_tcvis, tpi_outer_radius=100, tpi_inner_radius=0, device=DEFAULT_DEVICE)

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:

Name	Type	Description	Default
ds_merged	Dataset	The Planet scene optical data or Sentinel 2 scene optical dataset including data_masks.	required
ds_arcticdem	Dataset	The ArcticDEM dataset.	required
ds_tcvis	Dataset	The TCVIS dataset.	required
tpi_outer_radius	int	The outer radius of the annulus kernel for the tpi calculation in m. Defaults to 100m.	100
tpi_inner_radius	int	The inner radius of the annulus kernel for the tpi calculation in m. Defaults to 0.	0
device	Literal['cuda', 'cpu'] | int	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".	DEFAULT_DEVICE

Returns:

Type	Description
Dataset	xr.Dataset: The preprocessed dataset.

Source code in darts-preprocessing/src/darts_preprocessing/preprocess.py

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.

    """
    tick_fstart = time.perf_counter()
    logger.debug("Starting fast v1 preprocessing.")

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

    # Reproject TCVIS to optical data
    tick_sproj = time.perf_counter()
    ds_tcvis = ds_tcvis.odc.reproject(ds_merged.odc.geobox, resampling="cubic")
    tick_eproj = time.perf_counter()
    logger.debug(f"Reprojection of TCVIS done in {tick_eproj - tick_sproj:.2f} seconds.")

    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
    tick_sproj = time.perf_counter()
    ds_arcticdem = ds_arcticdem.odc.reproject(ds_merged.odc.geobox.buffered(tpi_outer_radius), resampling="cubic")
    tick_eproj = time.perf_counter()
    logger.debug(f"Reprojection of ArcticDEM done in {tick_eproj - tick_sproj:.2f} seconds.")

    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"

    tick_fend = time.perf_counter()
    logger.info(f"Preprocessing done in {tick_fend - tick_fstart:.2f} seconds.")
    return ds_merged