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Description:

georasterize 0.9.0

geo-rasterize: a 2D rasterizer for geospatial applications, written in Rust

geo-rasterize is a Python wrapper for a rust library with the same
name that rasterizes
shapely
vector shapes, just like
rasterio's
features.rasterize. The
difference is that while rasterio
is built on GDAL, this library has no dependencies -- you can install
it without having to worry about GDAL (or any other C library at all)!
Plus geo-rasterize's rasterization algorithm is based on GDAL's so
it should be a drop in replacement for rasterio.features.rasterize
and it offers a very similar API.
We publish wheels to PyPI Python 3.7+ for the following platforms:

Operating System
Architecture

Linux
x86-64

Linux
i686

Linux
aarch64

Windows
x86-64

Windows
i686

MacOS
Universal2 (x86-64 and aarch64)

Examples
For example, let's rasterize a single Point located at (x=1, y=2)
onto a raster that is 5 pixels wide and 6 pixels high. By default, the
raster pixels will start out with value zero, and we'll put a 1 in
every pixel the point touches:
>>> from shapely.geometry import Point
>>> from geo_rasterize import rasterize
>>> print(rasterize([Point(1, 2)], [1], (5, 6)))
[[0 0 0 0 0]
[0 0 0 0 0]
[0 1 0 0 0]
[0 0 0 0 0]
[0 0 0 0 0]
[0 0 0 0 0]]

And the result is just what we expect: a 5x6 numpy array with
exactly one pixel set to 1! Note that we had to specify a list of
shapes rather than just one shape. And the list of foreground values
([1] in this case) has to have the same length since we'll need one
foreground value for each shape.
So let's see multiple shapes!
>>> from shapely.geometry import Point, LineString
>>> from geo_rasterize import rasterize
>>> shapes = [Point(3, 4), LineString([(0, 3), (3, 0)])]
>>> foregrounds = [3, 7]
>>> raster_size = (4, 5)
>>> print(rasterize(shapes, foregrounds, raster_size))
[[0 0 7 0]
[0 7 7 0]
[7 7 0 0]
[7 0 0 0]
[0 0 0 3]]

What happens when two shapes burn in the same pixel? That depends on
how you set the merge algorithm, given by the algorithm
parameter. The default is 'replace' which means the last shape
overwrites the pixel but you can also set it to 'add' to that
foreground values will sum. That allows you to make heatmaps!
>>> from shapely.geometry import Point, LineString
>>> from geo_rasterize import rasterize
>>> shapes = [LineString([(0, 0), (5, 5)]), LineString([(5, 0), (0, 5)])]
>>> print(rasterize(shapes, [1, 1], (5, 5), algorithm='add'))
[[1 0 0 0 1]
[0 1 0 1 1]
[0 0 2 1 0]
[0 1 1 1 0]
[1 1 0 0 1]]

Our two lines cross at the center where you'll find 2.
You can change the default value using the background parameter. And
you can set the array dtype using the dtype parameter. Setting
dtype='uint8' will reduce the space needed for your raster array by
8x. This is especially useful if you're only interested in binary
rasterization.
Geographic transforms
All our examples so far have assumed that our shapes' coordinates are
in the image space. In other words, we've assumed that the x
coordinates will be in the range 0..width and the y coordinates
will be in the range 0..height. Alas, that is often not the case!
For satellite imagery (or remote sensing imagery in general), images
will almost always specify both a Coordinate Reference System
(CRS) and an
affine transformation in their metadata. See rasterio's
Georeferencing
for more details.
In order to work with most imagery, you have to convert your vector
shapes from whatever their original CRS is (often EPSG:4326 for
geographic longitude and latitude) into whatever CRS your data file
specifies (often a
UTM
projection but there are so many choices). Then, you need to apply an
affine transformation to convert from world coordinates to pixel
coordinates. Since raster imagery usually specifies the inverse
transformation matrix (i.e. a pix_to_geo transform), you'll first
need to invert it to get a geo_to_pix transform before applying it
to the coordinates. And now you've got pixel coordinates appropriate
for your image data!
geo-raterize can ease this tedious process by taking care of the
affine transformation. Just pass an affine transform array using the
geo_to_pix parameter (call .to_gdal() if you have an
affine.Affine instance).
To summarize, you'll have to:

extract the CRS from your image and convert your shapes into that
CRS (probably using pyproj
and its integration with [geo types][geo],
extract the pix_to_geo transform from your imagery metadata
create an Affine instance from that data (GDAL represents these
as a [f64; 6] array and you can use Affine.from_gdal)
call transform.inverse to get the corresponding geo_to_pix
transform (remember that not all transforms are invertible!)
call transform.to_gdal() and use the resulting array with the
geo_to_pix parameter