Raster to Vector Conversion and Vice Versa

Geospatial dataset conversion with Python. For interactive reading and executing code blocks Binder and find geo-conversion.ipynb, or install Python and JupyterLab locally.

The goal of this section is to guide to an understanding of conversions from raster and to vector data formats and vice versa.



  1. The core functions used in this e-book are introduced with the raster and vector data handling explanations and additionally implemented in the flusstools package.

  2. Download sample raster datasets from River Architect. This page uses GeoTIFF raster data located in RiverArchitect/SampleData/01_Conditions/2100_sample/.


Raster to Line

In this section, we convert the least cost path raster dataset (least_cost.tif) into a (poly) line shapefile. For this purpose we first write a function called offset2coords, which represents the inverse of the coords2offset function, to convert x/y offset (in pixel numbers) to coordinates of a geospatial dataset’s geo-transformation:

def offset2coords(geo_transform, offset_x, offset_y):
    # get origin and pixel dimensions from geo_transform (osgeo.gdal.Dataset.GetGeoTransform() object)
    origin_x = geo_transform[0]
    origin_y = geo_transform[3]
    pixel_width = geo_transform[1]
    pixel_height = geo_transform[5]
    # calculate x and y coordinates
    coord_x = origin_x + pixel_width * (offset_x + 0.5)
    coord_y = origin_y + pixel_height * (offset_y + 0.5)

    # return x and y coordinates
    return coord_x, coord_y


The offset is added 0.5 pixels in both x and y directions to meet the center of the pixel rather than the top left pixel corner.

Next we can write the core function to convert a raster dataset to a line shapefile. This function named raster2line:

  1. Opens a raster, its band as array and geo_transform (geo-transformation) defined with the raster_file_name argument and using the open_raster function.

  2. Calculates the maximum distance (max_distance) between two pixels that are considered connect-able, based on the hypothesis that the pixel height Δy and width Δx are the same: img

  3. Gets the trajectory of pixels that have a user parameter-defined pixel_value (e.g., 1 to trace 1-pixels in the binary least_cost.tif) and throws an error if the trajectory is empty (i.e., np.count_nonzero(trajectory) is 0).

  4. Uses the above define offset2coords function to append point coordinates to a points list.

  5. Creates a multi_line object (instance of ogr.Geometry(ogr.wkbMultiLineString)), which represents the (void) final least cost path.

  6. Iterates through all possible combinations of points (excluding combinations of points with themselves) with itertools.combinations(iterable, r=number-of-combinations=2).

    • Points are stored in the points list.

    • point1 and point2 are required to get the distance between pairs of points.

    • If the distance between the point is smaller than max_distance, the function creates a line object from the two points and appends it to the multi_line.

  7. Creates a new shapefile (named out_shp_fn) using the create_shp function (with integrated shapefile name length verification as per the geo_utils package).

  8. Adds the multi_line object as new feature to the shapefile (follows the descriptions on the shapefile page).

  9. Creates a .prj projection file (recall descriptions in the shapefile section) using the spatial reference system of the input raster with the get_srs function.

The raster2line function is also implemented in the flusstools.geotools script.

def raster2line(raster_file_name, out_shp_fn, pixel_value):
    Convert a raster to a line shapefile, where pixel_value determines line start and end points
    :param raster_file_name: STR of input raster file name, including directory; must end on ".tif"
    :param out_shp_fn: STR of target shapefile name, including directory; must end on ".shp"
    :param pixel_value: INT/FLOAT of a pixel value
    :return: None (writes new shapefile).

    # calculate max. distance between points
    # ensures correct neighbourhoods for start and end pts of lines
    raster, array, geo_transform = raster2array(raster_file_name)
    pixel_width = geo_transform[1]
    max_distance = np.ceil(np.sqrt(2 * pixel_width**2))

    # extract pixels with the user-defined pixel value from the raster array
    trajectory = np.where(array == pixel_value)
    if np.count_nonzero(trajectory) is 0:
        print("ERROR: The defined pixel_value (%s) does not occur in the raster band." % str(pixel_value))
        return None

    # convert pixel offset to coordinates and append to nested list of points
    points = []
    count = 0
    for offset_y in trajectory[0]:
        offset_x = trajectory[1][count]
        points.append(offset2coords(geo_transform, offset_x, offset_y))
        count += 1

    # create multiline (write points dictionary to line geometry (wkbMultiLineString)
    multi_line = ogr.Geometry(ogr.wkbMultiLineString)
    for i in itertools.combinations(points, 2):
        point1 = ogr.Geometry(ogr.wkbPoint)
        point1.AddPoint(i[0][0], i[0][1])
        point2 = ogr.Geometry(ogr.wkbPoint)
        point2.AddPoint(i[1][0], i[1][1])

        distance = point1.Distance(point2)
        if distance < max_distance:
            line = ogr.Geometry(ogr.wkbLineString)
            line.AddPoint(i[0][0], i[0][1])
            line.AddPoint(i[1][0], i[1][1])

    # write multiline (wkbMultiLineString2shp) to shapefile
    new_shp = create_shp(out_shp_fn, layer_name="raster_pts", layer_type="line")
    lyr = new_shp.GetLayer()
    feature_def = lyr.GetLayerDefn()
    new_line_feat = ogr.Feature(feature_def)

    # create projection file
    srs = get_srs(raster)
    make_prj(out_shp_fn, int(srs.GetAuthorityCode(None)))
    print("Success: Wrote %s" % str(out_shp_fn))

Now we can use the raster2line function to convert the least cost path from pixel (raster) format to line format:

source_raster_fn = r"" +  os.path.abspath('') + "/geodata/river-architect/least_cost.tif"
target_shp_fn = r"" + os.path.abspath('') + "/geodata/river-architect/least_cost.shp"
pixel_value = 1
raster2line(source_raster_fn, target_shp_fn, pixel_value)
Success: Wrote C:\Users\schwindt\jupyter\nb-lectures/geodata/river-architect/least_cost.shp



There is a little error in the least_cost line. Can you find the error? What can be done to fix the error?


Network routing is the core specialty of the NetworkX package (see Open source libraries). Read more about network analyses in Michael Diener’s GitHub pages.

Raster to Polygon

gdal comes with the powerful Polygonize functionality for the easy conversion of a raster dataset to a polygon shapefile. While gdal.Polygonize enables writing a simple raster2polygon function, it has a drawback, which is that it can only handle integer values and it merely randomly attributes FID values by default. Because the FID values are not meaningful, we can implement the following float2int function to preserve the original value range (uses the raster2array and create_raster functions explained in the raster section):

def float2int(raster_file_name, band_number=1):
    :param raster_file_name: STR of target file name, including directory; must end on ".tif"
    :param band_number: INT of the raster band number to open (default: 1)
    :output: new_raster_file_name (STR)
    # use raster2array function to get raster, np.array and the geo transformation
    raster, array, geo_transform = raster2array(raster_file_name, band_number=band_number)
    # convert np.array to integers
        array = array.astype(int)
    except ValueError:
        print("ERROR: Invalid raster pixel values.")
        return raster_file_name
    # get spatial reference system
    src_srs = get_srs(raster)
    # create integer raster    
    new_name = raster_file_name.split(".tif")[0] + "_int.tif"
    create_raster(new_name, array, epsg=int(src_srs.GetAuthorityCode(None)),
                  rdtype=gdal.GDT_Int32, geo_info=geo_transform)
    # return name of integer raster
    return new_name

The following raster2polygon function:

  1. Uses the float2int function to ensure that any raster file_name provided is converted to purely integer values.

  2. Creates a new shapefile (named out_shp_fn) using the create_shp function (with integrated shapefile name length verification as per the geo_utils package).

  3. Adds a new ogr.OFTInteger field (recall the field creation) in the shapefile section) named by the optional field_name input argument.

  4. Runs gdal.Polygonize with:

    • hSrcBand=raster_band

    • hMaskBand=None (optional raster band to define polygons)

    • hOutLayer=dst_layer

    • iPixValField=0 (if no field was be added, set to -1 in order to create FID field; if more field added, set to 1, 2, … )

    • papszOptions=[] (no effect for ESRI Shapefile driver type)

    • callback=None for not using the reporting algorithm (GDALProgressFunc())

  5. Creates a .prj projection file (recall descriptions in the shapefile section) using the spatial reference system of the input raster with the get_srs function.

def raster2polygon(file_name, out_shp_fn, band_number=1, field_name="values"):
    Convert a raster to polygon
    :param file_name: STR of target file name, including directory; must end on ".tif"
    :param out_shp_fn: STR of a shapefile name (with directory e.g., "C:/temp/poly.shp")
    :param band_number: INT of the raster band number to open (default: 1)
    :param field_name: STR of the field where raster pixel values will be stored (default: "values")
    :return: None
    # ensure that the input raster contains integer values only and open the input raster
    file_name = float2int(file_name)
    raster, raster_band = open_raster(file_name, band_number=band_number)

    # create new shapefile with the create_shp function
    new_shp = create_shp(out_shp_fn, layer_name="raster_data", layer_type="polygon")
    dst_layer = new_shp.GetLayer()

    # create new field to define values
    new_field = ogr.FieldDefn(field_name, ogr.OFTInteger)

    # Polygonize(band, hMaskBand[optional]=None, destination lyr, field ID, papszOptions=[], callback=None)
    gdal.Polygonize(raster_band, None, dst_layer, 0, [], callback=None)

    # create projection file
    srs = get_srs(raster)
    make_prj(out_shp_fn, int(srs.GetAuthorityCode(None)))
    print("Success: Wrote %s" % str(out_shp_fn))


Now we can use the raster2polygon function to convert the flow depth raster for 1000 cfs (h001000.cfs from the River Architect sample datasets) to a polygon shapefile:

src_raster = r"" +  os.path.abspath('') + "/geodata/river-architect/h001000.tif"
tar_shp = r"" + os.path.abspath('') + "/geodata/river-architect/h_poly_cls.shp"
raster2polygon(src_raster, tar_shp)
Success: Wrote C:\Users\schwindt\jupyter\nb-lectures/geodata/river-architect/h_poly_cls.shp


Rasterize (Vector Shapefile to Raster)

Similar to gdal.Polygonize, gdal.RasterizeLayer represents a powerful option to easily convert a shapefile into a raster. More precisely, a shapefile is not really converted but burned onto a raster. That means, values stored in a field of a shapefile feature are used (burned) as pixel values in a new raster. A little attention is required to ensure that the correct values and data types are used. So let’s write a rasterize function that we can use robustly over and over again, avoiding potential headaches. The rasterize function:

  1. Open the provided input shapefile name and its layer.

  2. Reads the spatial extent of the layer.

  3. Derives the solution as a function of the spatial extent and a user-defined pixel_size (optional argument).

  4. Creates a new GeoTIFF raster using the

    • user-defined output_raster_file_name,

    • calculated x and y resolution, and

    • eType (default is gdal.GDT_Float32 - recall all data type options listed in the raster section.

  5. Applies the geo-transformation defined by the source layer extents and the pixel_size.

  6. Creates one raster band, fills the band with the user-defined no_data_value (default is -9999), and sets the no_data_value.

  7. Sets the spatial reference system of the raster to the same as the source shapefile.

  8. Applies gdal.RasterizeLayer with

    • dataset=target_ds (target raster dataset),

    • bands=[1] (list(integer) - increase to defined more raster bands and assign other values, e.g., from other fields of the source shapefile),

    • layer=source_lyr (layer with features to burn to the raster),

    • pfnTransformer=None (read more in the gdal docs),

    • pTransformArg=None (read more in the gdal docs),

    • burn_values=[0] (a default value that is burned to the raster),

    • options=["ALL_TOUCHED=TRUE"] defines that all pixels touched by a polygon get the polygon’s field value - if not set: only pixels that are entirely in the polygon get a value assigned,

    • options=["ATTRIBUTE=" + str(kwargs.get("field_name"))] defines the field name with values to burn.

def rasterize(in_shp_file_name, out_raster_file_name, pixel_size=10, no_data_value=-9999,
              rdtype=gdal.GDT_Float32, **kwargs):
    Converts any shapefile to a raster
    :param in_shp_file_name: STR of a shapefile name (with directory e.g., "C:/temp/poly.shp")
    :param out_raster_file_name: STR of target file name, including directory; must end on ".tif"
    :param pixel_size: INT of pixel size (default: 10)
    :param no_data_value: Numeric (INT/FLOAT) for no-data pixels (default: -9999)
    :param rdtype: gdal.GDALDataType raster data type - default=gdal.GDT_Float32 (32 bit floating point)
    :kwarg field_name: name of the shapefile's field with values to burn to the raster
    :return: produces the shapefile defined with in_shp_file_name

    # open data source
        source_ds = ogr.Open(in_shp_file_name)
    except RuntimeError as e:
        print("Error: Could not open %s." % str(in_shp_file_name))
        return None
    source_lyr = source_ds.GetLayer()

    # read extent
    x_min, x_max, y_min, y_max = source_lyr.GetExtent()

    # get x and y resolution
    x_res = int((x_max - x_min) / pixel_size)
    y_res = int((y_max - y_min) / pixel_size)

    # create destination data source (GeoTIff raster)
    target_ds = gdal.GetDriverByName('GTiff').Create(out_raster_file_name, x_res, y_res, 1, eType=rdtype)
    target_ds.SetGeoTransform((x_min, pixel_size, 0, y_max, 0, -pixel_size))
    band = target_ds.GetRasterBand(1)

    # get spatial reference system and assign to raster
    srs = get_srs(source_ds)
    except RuntimeError as e:
        return None

    # RasterizeLayer(Dataset dataset, int bands, Layer layer, pfnTransformer=None, pTransformArg=None,
    # int burn_values=0, options=None, GDALProgressFunc callback=0, callback_data=None)
    gdal.RasterizeLayer(target_ds, [1], source_lyr, None, None, burn_values=[0],
                                options=["ALL_TOUCHED=TRUE", "ATTRIBUTE=" + str(kwargs.get("field_name"))])

    # release raster band


Rasterize can also be run as a terminal command with gdal_rasterize.

Now we can use the rasterize function to convert the above polygonized flow depth polygon shapefile (h_poly_cls.shp) back to a raster (that is a little bit useless in practice, but an illustrative exercise). Pay attention to the data type, which is gdal.GDT_Int32 and define the field_name correctly.

src_shp = r"" + os.path.abspath('') + "/geodata/river-architect/h_poly_cls.shp"
tar_ras = r"" +  os.path.abspath('') + "/geodata/river-architect/h_re_rastered.tif"
rasterize(src_shp, tar_ras, pixel_size=5, rdtype=gdal.GDT_Int32, field_name="values")



Get more familiar with the conversion of rasters and shapefiles in the geospatial ecohydraulics exercise.