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darts_acquisition.load_planet_masks

Load the valid and quality data masks from a Planet scene.

Parameters:

  • fpath (str | pathlib.Path) –

    The file path to the Planet scene from which to derive the masks.

Raises:

  • FileNotFoundError –

    If no matching UDM-2 TIFF file is found in the specified path.

Returns:

  • xarray.Dataset –

    xr.Dataset: A merged xarray Dataset containing two data masks: - 'valid_data_mask': A mask indicating valid (1) and no data (0). - 'quality_data_mask': A mask indicating high quality (1) and low quality (0).

Source code in darts-acquisition/src/darts_acquisition/planet.py 
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@stopuhr.funkuhr("Loading Planet masks", printer=logger.debug, print_kwargs=True)
def load_planet_masks(fpath: str | Path) -> xr.Dataset:
    """Load the valid and quality data masks from a Planet scene.

    Args:
        fpath (str | Path): The file path to the Planet scene from which to derive the masks.

    Raises:
        FileNotFoundError: If no matching UDM-2 TIFF file is found in the specified path.

    Returns:
        xr.Dataset: A merged xarray Dataset containing two data masks:
            - 'valid_data_mask': A mask indicating valid (1) and no data (0).
            - 'quality_data_mask': A mask indicating high quality (1) and low quality (0).

    """
    # Convert to Path object if a string is provided
    fpath = fpath if isinstance(fpath, Path) else Path(fpath)

    logger.debug(f"Loading data masks from {fpath.resolve()}")

    # Get imagepath
    udm_path = next(fpath.glob("*_udm2.tif"), None)
    if not udm_path:
        udm_path = next(fpath.glob("*_udm2_clip.tif"), None)
    if not udm_path:
        raise FileNotFoundError(f"No matching UDM-2 TIFF files found in {fpath.resolve()} (.glob('*_udm2.tif'))")

    # See udm classes here: https://developers.planet.com/docs/data/udm-2/
    da_udm = xr.open_dataarray(udm_path)

    invalids = da_udm.sel(band=8).fillna(0) != 0
    low_quality = da_udm.sel(band=[2, 3, 4, 5, 6]).max(axis=0) == 1
    high_quality = ~low_quality & ~invalids
    qa_ds = xr.Dataset(coords={c: da_udm.coords[c] for c in da_udm.coords})
    qa_ds["quality_data_mask"] = (
        xr.zeros_like(da_udm.sel(band=8)).where(invalids, 0).where(low_quality, 1).where(high_quality, 2)
    )
    qa_ds["quality_data_mask"].attrs = {
        "data_source": "planet",
        "long_name": "Quality data mask",
        "description": "0 = Invalid, 1 = Low Quality, 2 = High Quality",
    }
    return qa_ds