Given the following .vrt file which I manually created:

    <OGRVRTLayer name="DGM5_BE">
        <GeometryField encoding="PointFromColumns" x="x" y="y" z="d"/>

and the associated DGM5_BE.txt - here are the first lines:


I run the following command to convert the DGM5:

$ gdal_grid -of GTiff -ot Float32 -l DGM5_BE DGM5_BE.vrt DGM5_BE.tif

This fails with the following error message:

ERROR 1: Failed to open datasource `DGM5_BE.txt'.
No point geometry found on layer DGM5_BE, skipping.


From the CSV driver documentation:

Starting with GDAL 1.8.0, for files structured as CSV, but not ending with .CSV extension, the 'CSV:' prefix can be added before the filename to force loading by the CSV driver.

Either rename DGM5_BE.txt to DGM5_BE.csv or change the <SrcDataSource> element to:

  • Thx. I can't believe how long it takes to calculate a TIFF! I already added --config GDAL_NUM_THREADS ALL_CPUS. Is there recommendable format other than TIFF which can be used in QGIS?
    – JJD
    Jul 4 '14 at 13:47
  • The problem is that triangulation is only a single thread process, irrespective of how many CPU threads you allow this process it can only use 1 to triangulate with the other threads for reading/writing. There are options that do slow down a TIFF, what compression are you using? I don't think you will get any more performance from this process regardless of the output format. Aug 9 '18 at 3:58

For what its worth I created a demo that shows example usage of gdal_grid.

def main():
    Demo using gdal_grid


    from os.path import exists
    import ubelt as ub
    import geopandas as gpd
    import numpy as np

    # Generate Demo Data Ungridded data
    # Generate a random value at points along the African continent
    xyz = []
    world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
    rng = np.random.RandomState(0)
    for shape in world[world['continent'] == 'Africa'].geometry:
        if shape.type == 'MultiPolygon':
            for geom in shape.geoms:
                for x, y in geom.exterior.coords:
                    z = rng.rand()
                    xyz.append((x, y, z))
        elif shape.type == 'Polygon':
            for x, y in shape.exterior.coords:
                z = rng.rand()
                xyz.append((x, y, z))
    xyz = np.array(xyz)

    # Write the demo data to a CSV file
    lines = ['x;y;d']
    for x, y, z in xyz:
    text = '\n'.join(lines)
    with open('tmp.csv', 'w') as file:

    # I was unable to get the command to work with the CSV file alone
    # I needed to create a VRT that pointed at the CVS file.
    with open('tmp.vrt', 'w') as file:
                <OGRVRTLayer name="tmp">
                    <GeometryField encoding="PointFromColumns" x="x" y="y" z="d"/>

    # Can modify these but they are not needed
    minx, miny = xyz.T[0:2].min(axis=1)
    maxx, maxy = xyz.T[0:2].max(axis=1)
    lonext = maxx - minx
    latext = maxy - miny
    ar = latext / lonext
    xres = int(512)
    yres = int(xres * ar)

    # Call the GDAL Grid command
    command = (
        'gdal_grid '
        # '-ot Float32 -of GTiff '
        # '-zfield d '
        # '-a_srs EPSG:4326 '
        # f'-txe {minx - 1} {maxy + 1} '
        # f'-tye {miny - 1} {maxx + 1} '
        # f'-outsize {xres} {yres} '
        # '-a invdist:power=2.0:smoothing=1.0 '
        # '-a nearest:radius1=1.0:radius2=1.0 '
        # '-a linear:radius=1.0 '
        'tmp.vrt tmp.tif')

    info = ub.cmd(command, verbose=3)
    assert info['ret'] == 0

    if exists('tmp.tif'):
        # Show the result if it worked
        import tifffile
        data = tifffile.imread('tmp.tif')
        from matplotlib import pyplot as plt
        print('data.shape = {!r}'.format(data.shape))

The result is this:

enter image description here

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