Python Rasterio – How to Rasterize by Attribute Using GDAL

gdalpythonrasterio

According to Rasterizing shapefiles with GDAL and Python? it seems that gdal allows rasterization of a layer according to a certain attribute

ds = gdal.RasterizeLayer(target_ds, [1], source_layer, options = ["ATTRIBUTE=ID"])

I would like to rasterize a layer with small square polygon geometries using rasterio

rasterio has a features.rasterize method (see docs), but it seems to only be able to output numpy arrays with 1's where the polygons occur. Since rasterio is a higher level library than gdal I am a bit surprised that selecting an attribute to burn the raster with is not supported functionality.

Is there any built-in method in rasterio to burn a raster by an attribute from the original vector layer?

Best Answer

The inputs to features.rasterize include

shapes (iterable of (geometry, value) pairs or iterable over) – geometries. geometry can either be an object that implements the geo interface or GeoJSON-like object.

So you can just pass tuples of (geom, value), where value is the attribute value you wish to burn into the raster.

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[GIS] Fast raster combine using rasterio

Looking over your code, I don't see how it will be faster than pure Python. It's exclusively calling Python methods (rasterio methods, np.dstack is Python) and those aren't executed any faster just because the function is compiled with Cython.

The key to speeding things up is in here:

    # Iterate over rows and columns in this block
    for x in xrange(x_wsize):
        for y in xrange(y_wsize):
            t = tuple(in_arr[x, y])
            if t not in d:
                d[t] = index 
                index += 1
            out_arr[x_start + x, y_start + y] = d[t]

See if you can change this from a slow Python loop over all pixels in the array to code that exploits Numpy's much faster array operations. It'll be tricky, no doubt, because of the index accounting, but I think you could trade memory in the form of more numpy arrays/masks for improved speed.

Alternatively, optimize it for Cython by using cdef for all the variables in the loop, particularly x and y and using typed memory views for array access. I think you might find rasterio's io_multi_cfloat64 a useful example.

GDAL – Accessing File Geodatabase (.gdb) Rasters via GDAL in Python

Reading and writing file geodatabase rasters is not supported by the File Geodatabase API as of July 2015. According to this 2010 Esri blog File Geodatabase API details:

Rasters (Raster Dataset, Raster Catalog, Mosaic Datasets and Raster Attributes) are not supported with the initial release of the File Geodatabase API.

Additionally, according to an Esri Staff member on a Geonet discussion (June 2015):

Currently we do not have any plans on supported access to raster data via the File Geodatabase API. If you feel this is needed, please add it to the Esri Ideas site and it will be considered for a…

Best Answer

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GDAL – Accessing File Geodatabase (.gdb) Rasters via GDAL in Python

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