vectorize

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vectorize#

vectorize(xds: str | CloudPath | Path | tuple[ndarray | MaskedArray, dict] | DataArray | Dataset | DatasetReader | DatasetWriter, values: None | int | list = None, keep_values: bool = True, dissolve: bool = False, default_nodata: int = 0) → GeoDataFrame[source]#

Vectorize a xarray to get the class vectors.

If dissolved is False, it returns a GeoDataFrame with a GeoSeries per cluster of pixel value, with the value as an attribute. Else it returns a GeoDataFrame with a unique polygon.

Warning

  • Your data is casted by force into np.uint8, so be sure that your data is classified.

  • This could take a while as the computing time directly depends on the number of polygons to vectorize.

Please be careful.

Parameters:

  • xds (AnyRasterType) – Path to the raster or a rasterio dataset or a xarray

  • values (Union[None, int, list]) – Get only the polygons concerning this/these particular values

  • keep_values (bool) – Keep the passed values. If False, discard them and keep the others.

  • dissolve (bool) – Dissolve all the polygons into one unique. Only works if values are given.

  • default_nodata (int) – Default values for nodata in case of non existing in file

Returns:

Classes Vector

Return type:

gpd.GeoDataFrame

Example

>>> raster_path = "path/to/raster.tif"
>>> vec1 = vectorize(raster_path)
>>>
>>> # or
>>> with rasterio.open(raster_path) as ds:
>>>     vec2 = vectorize(ds)
>>>
>>> # Assert those two approaches give the same result
>>> vec1 == vec2
True
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