Export Reference

darts_export

Dataset export for the DARTS dataset.

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

export_arcticdem_datamask(tile, out_dir)

Export the arcticdem data mask as a GeoTIFF file.

Parameters:

Name	Type	Description	Default
tile	Dataset	The inference result.	required
out_dir	Path	The path where to export to.	required

Source code in darts-export/src/darts_export/inference.py

def export_arcticdem_datamask(tile: xr.Dataset, out_dir: Path):
    """Export the arcticdem data mask as a GeoTIFF file.

    Args:
        tile (xr.Dataset): The inference result.
        out_dir (Path): The path where to export to.

    """
    fpath = out_dir / "arcticdem_data_mask.tif"
    with stopuhr(f"Exporting arcticdem data mask to {fpath}", logger.debug):
        tile["arcticdem_data_mask"].rio.to_raster(fpath, driver="GTiff", compress="LZW")

export_binarized(tile, out_dir, export_ensemble_inputs=False, tags={})

Export the binarized segmentation layer to a file.

If export_ensemble_inputs is set to True and the ensemble used at least two models for inference, the binarized segmentation of the models will be written as individual files as well.

Parameters:

Name	Type	Description	Default
tile	Dataset	The inference result.	required
out_dir	Path	The path where to export to.	required
export_ensemble_inputs	bool	Also save the model outputs, not only the ensemble result. Only applies if the inference result is an ensemble result and has at least two inputs. Defaults to False.	False
tags	dict	optional GeoTIFF metadata to be written. Defaults to no additional metadata.	{}

Source code in darts-export/src/darts_export/inference.py

def export_binarized(tile: xr.Dataset, out_dir: Path, export_ensemble_inputs: bool = False, tags: dict = {}):
    """Export the binarized segmentation layer to a file.

    If `export_ensemble_inputs` is set to True and the ensemble used at least two models for inference,
    the binarized segmentation of the models will be written as individual files as well.

    Args:
        tile (xr.Dataset): The inference result.
        out_dir (Path): The path where to export to.
        export_ensemble_inputs (bool, optional): Also save the model outputs, not only the ensemble result.
            Only applies if the inference result is an ensemble result and has at least two inputs.
            Defaults to False.
        tags (dict, optional): optional GeoTIFF metadata to be written. Defaults to no additional metadata.

    """
    subset_names = _get_subset_names(tile)
    if export_ensemble_inputs and len(subset_names) > 1:
        for subset in _get_subset_names(tile):
            tick_estart = time.perf_counter()
            layer_name = f"binarized_segmentation-{subset}"
            fpath = out_dir / f"{layer_name}.tif"
            tile[layer_name].rio.to_raster(fpath, driver="GTiff", tags=tags, compress="LZW")
            tick_eend = time.perf_counter()
            logger.debug(f"Exported binarized segmentation for {subset} to {fpath} in {tick_eend - tick_estart:.2f}s")

    fpath = out_dir / "binarized.tif"
    with stopuhr(f"Exporting binarized segmentation to {fpath}", logger.debug):
        tile["binarized_segmentation"].rio.to_raster(fpath, driver="GTiff", tags=tags, compress="LZW")

export_datamask(tile, out_dir)

Export the data mask as a GeoTIFF file.

Parameters:

Name	Type	Description	Default
tile	Dataset	The inference result.	required
out_dir	Path	The path where to export to.	required

Source code in darts-export/src/darts_export/inference.py

def export_datamask(tile: xr.Dataset, out_dir: Path):
    """Export the data mask as a GeoTIFF file.

    Args:
        tile (xr.Dataset): The inference result.
        out_dir (Path): The path where to export to.

    """
    fpath = out_dir / "data_mask.tif"
    with stopuhr(f"Exporting data mask to {fpath}", logger.debug):
        tile["quality_data_mask"].rio.to_raster(fpath, driver="GTiff", compress="LZW")

export_dem(tile, out_dir)

Export the DEM data as a GeoTIFF file.

Parameters:

Name	Type	Description	Default
tile	Dataset	The inference result.	required
out_dir	Path	The path where to export to.	required

Source code in darts-export/src/darts_export/inference.py

def export_dem(tile: xr.Dataset, out_dir: Path):
    """Export the DEM data as a GeoTIFF file.

    Args:
        tile (xr.Dataset): The inference result.
        out_dir (Path): The path where to export to.

    """
    fpath = out_dir / "dem.tif"
    with stopuhr(f"Exporting DEM data to {fpath}", logger.debug):
        tile[["slope", "relative_elevation"]].rio.to_raster(fpath, driver="GTiff", compress="LZW")

export_extent(tile, out_dir)

Export the extent of the prediction as a vector dataset in GeoPackage and GeoParquet format.

Parameters:

Name	Type	Description	Default
tile	Dataset	The inference result.	required
out_dir	Path	The path where to export to.	required

Source code in darts-export/src/darts_export/inference.py

def export_extent(tile: xr.Dataset, out_dir: Path):
    """Export the extent of the prediction as a vector dataset in GeoPackage and GeoParquet format.

    Args:
        tile (xr.Dataset): The inference result.
        out_dir (Path): The path where to export to.

    """
    fpath_gpkg = out_dir / "prediction_extent.gpkg"
    fpath_parquet = out_dir / "prediction_extent.parquet"
    with stopuhr(f"Exporting extent to {fpath_gpkg} and {fpath_parquet}", logger.debug):
        polygon_gdf = vectorization.vectorize(tile, "quality_data_mask", minimum_mapping_unit=0)
        polygon_gdf.to_file(fpath_gpkg, layer="prediction_extent")
        polygon_gdf.to_parquet(fpath_parquet)

export_optical(tile, out_dir)

Export the optical data as a GeoTIFF file.

Parameters:

Name	Type	Description	Default
tile	Dataset	The inference result.	required
out_dir	Path	The path where to export to.	required

Source code in darts-export/src/darts_export/inference.py

def export_optical(tile: xr.Dataset, out_dir: Path):
    """Export the optical data as a GeoTIFF file.

    Args:
        tile (xr.Dataset): The inference result.
        out_dir (Path): The path where to export to.

    """
    fpath = out_dir / "optical.tif"
    with stopuhr(f"Exporting optical data to {fpath}", logger.debug):
        tile[["red", "green", "blue", "nir"]].rio.to_raster(fpath, driver="GTiff", compress="LZW")

export_polygonized(tile, out_dir, export_ensemble_inputs=False, minimum_mapping_unit=32)

Export the binarized probabilities as a vector dataset in GeoPackage and GeoParquet format.

If export_ensemble_inputs is set to True and the ensemble used at least two models for inference, the vectorized binarized segmentation of the models will be written as individual files as well.

Parameters:

Name	Type	Description	Default
tile	Dataset	The inference result.	required
out_dir	Path	The path where to export to.	required
export_ensemble_inputs	bool	Also save the model outputs, not only the ensemble result. Only applies if the inference result is an ensemble result and has at least two inputs. Defaults to False.	False
minimum_mapping_unit	int	segments covering less pixel are removed. Defaults to 32.	32

Source code in darts-export/src/darts_export/inference.py

def export_polygonized(
    tile: xr.Dataset, out_dir: Path, export_ensemble_inputs: bool = False, minimum_mapping_unit: int = 32
):
    """Export the binarized probabilities as a vector dataset in GeoPackage and GeoParquet format.

    If `export_ensemble_inputs` is set to True and the ensemble used at least two models for inference,
    the vectorized binarized segmentation of the models will be written as individual files as well.

    Args:
        tile (xr.Dataset): The inference result.
        out_dir (Path): The path where to export to.
        export_ensemble_inputs (bool, optional): Also save the model outputs, not only the ensemble result.
            Only applies if the inference result is an ensemble result and has at least two inputs.
            Defaults to False.
        minimum_mapping_unit (int, optional): segments covering less pixel are removed. Defaults to 32.

    """
    subset_names = _get_subset_names(tile)
    if export_ensemble_inputs and len(subset_names) > 1:
        for subset in _get_subset_names(tile):
            tick_estart = time.perf_counter()
            layer_name = f"binarized_segmentation-{subset}"
            fpath_gpkg = out_dir / f"prediction_segments-{subset}.gpkg"
            fpath_parquet = out_dir / f"prediction_segments-{subset}.parquet"
            polygon_gdf = vectorization.vectorize(tile, layer_name, minimum_mapping_unit=minimum_mapping_unit)
            polygon_gdf.to_file(fpath_gpkg, layer=f"prediction_segments-{subset}")
            polygon_gdf.to_parquet(fpath_parquet)
            tick_eend = time.perf_counter()
            logger.debug(
                f"Exported binarized segmentation for {subset} to {fpath_gpkg} and {fpath_parquet}"
                f" in {tick_eend - tick_estart:.2f}s"
            )

    fpath_gpkg = out_dir / "prediction_segments.gpkg"
    fpath_parquet = out_dir / "prediction_segments.parquet"
    with stopuhr(f"Exporting binarized segmentation to {fpath_gpkg} and {fpath_parquet}", logger.debug):
        polygon_gdf = vectorization.vectorize(tile, "binarized_segmentation", minimum_mapping_unit=minimum_mapping_unit)
        polygon_gdf.to_file(fpath_gpkg, layer="prediction_segments")
        polygon_gdf.to_parquet(fpath_parquet)

export_probabilities(tile, out_dir, export_ensemble_inputs=False, tags={})

Export the probabilities layer to a file.

If export_ensemble_inputs is set to True and the ensemble used at least two models for inference, the probabilities of the models will be written as individual files as well.

Parameters:

Name	Type	Description	Default
tile	Dataset	The inference result.	required
out_dir	Path	The path where to export to.	required
export_ensemble_inputs	bool	Also save the model outputs, not only the ensemble result. Only applies if the inference result is an ensemble result and has at least two inputs. Defaults to False.	False
tags	dict	optional GeoTIFF metadata to be written. Defaults to no additional metadata.	{}

Source code in darts-export/src/darts_export/inference.py

def export_probabilities(tile: xr.Dataset, out_dir: Path, export_ensemble_inputs: bool = False, tags: dict = {}):
    """Export the probabilities layer to a file.

    If `export_ensemble_inputs` is set to True and the ensemble used at least two models for inference,
    the probabilities of the models will be written as individual files as well.

    Args:
        tile (xr.Dataset): The inference result.
        out_dir (Path): The path where to export to.
        export_ensemble_inputs (bool, optional): Also save the model outputs, not only the ensemble result.
            Only applies if the inference result is an ensemble result and has at least two inputs.
            Defaults to False.
        tags (dict, optional): optional GeoTIFF metadata to be written. Defaults to no additional metadata.

    """
    subset_names = _get_subset_names(tile)
    if export_ensemble_inputs and len(subset_names) > 1:
        for subset in _get_subset_names(tile):
            tick_estart = time.perf_counter()
            layer_name = f"probabilities-{subset}"
            fpath = out_dir / f"{layer_name}.tif"
            tile[layer_name].rio.to_raster(fpath, driver="GTiff", tags=tags, compress="LZW")
            tick_eend = time.perf_counter()
            logger.debug(f"Exported probabilities for {subset} to {fpath} in {tick_eend - tick_estart:.2f}s")

    fpath = out_dir / "probabilities.tif"
    with stopuhr(f"Exporting probabilities to {fpath}", logger.debug):
        tile["probabilities"].rio.to_raster(fpath, driver="GTiff", tags=tags, compress="LZW")

export_tcvis(tile, out_dir)

Export the TCVIS data as a GeoTIFF file.

Parameters:

Name	Type	Description	Default
tile	Dataset	The inference result.	required
out_dir	Path	The path where to export to.	required

Source code in darts-export/src/darts_export/inference.py

def export_tcvis(tile: xr.Dataset, out_dir: Path):
    """Export the TCVIS data as a GeoTIFF file.

    Args:
        tile (xr.Dataset): The inference result.
        out_dir (Path): The path where to export to.

    """
    fpath = out_dir / "tcvis.tif"
    with stopuhr(f"Exporting TCVIS data to {fpath}", logger.debug):
        tile[["tc_brightness", "tc_greenness", "tc_wetness"]].rio.to_raster(fpath, driver="GTiff", compress="LZW")

export_thumbnail(tile, out_dir)

Export a thumbnail of the optical data.

Parameters:

Name	Type	Description	Default
tile	Dataset	The inference result.	required
out_dir	Path	The path where to export to.	required

Source code in darts-export/src/darts_export/inference.py

def export_thumbnail(tile: xr.Dataset, out_dir: Path):
    """Export a thumbnail of the optical data.

    Args:
        tile (xr.Dataset): The inference result.
        out_dir (Path): The path where to export to.

    """
    fpath = out_dir / "thumbnail.jpg"
    with stopuhr(f"Exporting thumbnail to {fpath}", logger.debug):
        fig = thumbnail(tile)
        fig.savefig(fpath)
        fig.clear()