rusterize 0.8.1

High performance rasterization tool for Python built in Rust

High performance rasterization tool for Python built in Rust, inspired by the fasterize package with lots of useful improvements (see API).

rusterize is designed to work on all shapely geometries, even when they are nested inside complex geometry collections. Functionally, it supports four input types:

• geopandas GeoDataFrame and GeoSeries
• polars-st GeoDataFrame
• Python list of geometries in shapely.Geometry, WKB, or WKT format
• Numpy array of geometries in shapely.Geometry, WKB, or WKT format

It returns a xarray, a numpy, or a sparse array in COOrdinate format.

Installation

rusterize comes with numpy as the only required dependency and is distributed in different flavors. A core library that performs the rasterization and returns a bare numpy array, a xarray flavor that returns a georeferenced xarray (requires xarray and rioxarray and is the recommended flavor), or an all flavor with dependencies for all supported inputs.

Install the current version with pip:

# core library
pip install rusterize

# xarray capabilities
pip install 'rusterize[xarray]'

# support all input types
pip install 'rusterize[all]'

Contributing

Any contribution is welcome! You can install rusterize directly from this repo using maturin as an editable package. For this to work, you’ll need to have Rust and cargo installed. To run the tests you need to have gdal installed as well as the rusterize[all] flavor.

# clone repo
git clone https://github.com/<username>/rusterize.git
cd rusterize

# install Rust nightly toolchain
rustup toolchain install nightly-2026-01-09

# install maturin
pip install maturin

# install editable version with optmized code
maturin develop --profile dist-release

# test the new contribution
pytest

API

rusterize has a simple API consisting of a single function rusterize():

from rusterize import rusterize

rusterize(
    data,
    like=None,
    res=(30, 30),
    out_shape=(10, 10),
    extent=(0, 10, 10, 20),
    field="field",
    by="by",
    burn=None,
    fun="sum",
    background=0,
    encoding="xarray",
    all_touched=False,
    tap=False,
    dtype="uint8"
)

• data : geopandas.GeoDataFrame, geopandas.GeoSeries, polars.DataFrame, list, numpy.ndarray
  Input data to rasterize.

  • If polars.DataFrame, it must be have a "geometry" column with geometries stored in WKB or WKT format.
  • If list or numpy.ndarray, geometries must be in WKT, WKB, or shapely formats (EPSG is not inferred and defaults to None).

• like : xarray.DataArray or xarray.Dataset (default: None)
  Template array used as a spatial blueprint (resolution, shape, extent). Mutually exclusive with res, out_shape, and extent. Requires xarray and rioxarray.

• res : tuple or list (default: None)
  Pixel resolution defined as (xres, yres).

• out_shape : tuple or list (default: None)
  Output raster dimensions defined as (nrows, ncols).

• extent : tuple or list (default: None)
  Spatial bounding box defined as (xmin, ymin, xmax, ymax).

• field : str (default: None)
  Column name to use for pixel values. Mutually exclusive with burn. Not considered when input is list or numpy.ndarray.

• by : str (default: None)
  Column used for grouping. Each group is rasterized into a distinct band in the output. Not considered when input is list or numpy.ndarray.

• burn : int, float, or numpy.ndarray (default: None)
  A static value or a list of values to apply to each geometries. If a numpy.ndarray, it must match the length of the geometry data. Mutually exclusive with field. If burn is a numpy.ndarray, its dtype should match the output dtype, otherwise it is internally casted. If data is a geopandas.GeoSeries, its index is used as burn value, unless otherwise specified.

• fun : str (default: "last")
  Pixel function to use when burning geometries. Available options: sum, first, last, min, max, count, or any.

• background : int or float (default: numpy.nan)
  Value assigned to pixels not covered by any geometry.

• encoding : str (default: "xarray")
  The format of the returned object: "xarray", "numpy", or "sparse".

• all_touched : bool (default: False)
  If True, every pixel touched by a geometry is burned.

• tap : bool (default: False)
  Target Aligned Pixel: aligns the extent to the pixel resolution.

• dtype : str (default: "float64")
  Output data type (e.g., uint8, int32, float32).

Note that control over the desired extent is not as strict as for resolution and shape. That is, when resolution, output shape, and extent are specified, priority is given to resolution and shape. So, extent is not guaranteed, but resolution and shape are. If extent is not given, it is taken from the polygons and is not modified, unless you specify a resolution value. If you only specify an output shape, the extent is maintained. This mimics the logics of gdal_rasterize.

Encoding

rusterize offers three encoding options for the rasterization output. You can return a xarray/numpy with the rasterized geometries, or a new SparseArray structure. This SparseArray structure stores the band/row/column triplets of where the geometries should be burned onto the final raster, as well as their corresponding values before applying any pixel function. This can be used as an intermediate output to avoid allocating memory before materializing the final raster, or as a final product. SparseArray has three convenience functions: to_xarray(), to_numpy(), and to_frame(). The first two return the final xarray/numpy with the appropriate pixel function, the last returns a polars dataframe with only the coordinates and values of the rasterized geometries. Note that SparseArray avoids allocating memory for the array during rasterization until it's actually needed (e.g. calling to_xarray()). See below for an example.

Usage

from rusterize import rusterize
import geopandas as gpd
from shapely import wkt
import matplotlib.pyplot as plt

# construct geometries
geoms = [
    "POLYGON ((-180 -20, -140 55, 10 0, -140 -60, -180 -20), (-150 -20, -100 -10, -110 20, -150 -20))",
    "POLYGON ((-10 0, 140 60, 160 0, 140 -55, -10 0))",
    "POLYGON ((-125 0, 0 60, 40 5, 15 -45, -125 0))",
    "MULTILINESTRING ((-180 -70, -140 -50), (-140 -50, -100 -70), (-100 -70, -60 -50), (-60 -50, -20 -70), (-20 -70, 20 -50), (20 -50, 60 -70), (60 -70, 100 -50), (100 -50, 140 -70), (140 -70, 180 -50))",
    "GEOMETRYCOLLECTION (POINT (50 -40), POLYGON ((75 -40, 75 -30, 100 -30, 100 -40, 75 -40)), LINESTRING (60 -40, 80 0), GEOMETRYCOLLECTION (POLYGON ((100 20, 100 30, 110 30, 110 20, 100 20))))"
]

# create a GeoDataFrame with shapely geometries from WKT
gdf = gpd.GeoDataFrame({'value': range(1, len(geoms) + 1)}, geometry=wkt.loads(geoms), crs='EPSG:32619')

# or pass values directly to rusterize
# rusterize to "xarray" -> returns a xarray with the burned geometries and spatial reference when available (default)
# will raise a ModuleNotFoundError if xarray and rioxarray are not found
output = rusterize(
    geoms,
    res=(1, 1),
    fun="sum",
    burn=np.arange(1, len(geoms) + 1)
).squeeze()

output = rusterize(
    gdf,
    res=(1, 1),
    field="value",
    fun="sum",
).squeeze()

# plot it
fig, ax = plt.subplots(figsize=(12, 6))
output.plot.imshow(ax=ax)
plt.show()

# rusterize to "sparse" -> custom structure storing the coordinates and values of the rasterized geometries
output = rusterize(
    gdf,
    res=(1, 1),
    field="value",
    fun="sum",
    encoding="sparse"
)
output
# SparseArray:
# - Shape: (131, 361)
# - Extent: (-180.5, -70.5, 180.5, 60.5)
# - Resolution: (1.0, 1.0)
# - EPSG: 32619
# - Estimated size: 378.33 KB

# materialize into xarray or numpy
array = output.to_xarray()
array = output.to_numpy()

# get only coordinates and values
output.to_frame()
# shape: (29_340, 3)
# ┌─────┬─────┬──────┐
# │ row ┆ col ┆ data │
# │ --- ┆ --- ┆ ---  │
# │ u32 ┆ u32 ┆ f64  │
# ╞═════╪═════╪══════╡
# │ 6   ┆ 40  ┆ 1.0  │
# │ 6   ┆ 41  ┆ 1.0  │
# │ 6   ┆ 42  ┆ 1.0  │
# │ 7   ┆ 39  ┆ 1.0  │
# │ 7   ┆ 40  ┆ 1.0  │
# │ …   ┆ …   ┆ …    │
# │ 64  ┆ 258 ┆ 1.0  │
# │ 63  ┆ 259 ┆ 1.0  │
# │ 62  ┆ 259 ┆ 1.0  │
# │ 61  ┆ 260 ┆ 1.0  │
# │ 60  ┆ 260 ┆ 1.0  │
# └─────┴─────┴──────┘

Benchmarks

rusterize is fast! Let’s try it on small and large datasets in comparison to GDAL (benchmark_rusterize.py). You can run this with pytest and pytest-benchmark:

pytest <python file> --benchmark-min-rounds=10 --benchmark-time-unit='s'

--------------------------------------------- benchmark: 8 tests -------------------------------------------------
Name (time in s)               Min     Max    Mean  StdDev  Median     IQR  Outliers       OPS  Rounds  Iterations
------------------------------------------------------------------------------------------------------------------
test_water_small_f64_numpy  0.0038  0.0045  0.0040  0.0001  0.0040  0.0002      56;3  248.7981     181           1
test_water_small_f64        0.0048  0.0057  0.0050  0.0001  0.0050  0.0001      21;9  198.8759     158           1
test_water_small_gdal_f64   0.0053  0.0057  0.0054  0.0001  0.0054  0.0001     28;14  184.3595     160           1
test_water_large_f64_numpy  1.2628  1.3610  1.3133  0.0314  1.3193  0.0498       5;0    0.7614      10           1
test_water_large_f64        1.2762  1.4723  1.3342  0.0628  1.3149  0.0165       2;4    0.7495      10           1
test_water_large_gdal_f64   1.4128  1.4229  1.4178  0.0029  1.4180  0.0040       3;0    0.7053      10           1
test_roads_uint8            3.3184  3.5184  3.4021  0.0578  3.3849  0.0527       3;1    0.2939      10           1
test_roads_gdal_uint8       9.0672  9.1040  9.0901  0.0109  9.0920  0.0125       2;0    0.1100      10           1
------------------------------------------------------------------------------------------------------------------

And fasterize (benchmark_fasterize.r). Note that it doesn't support custom dtype so the returning raster is float64.

Unit: seconds
            expr              min           lq        mean       median          uq         max neval
 fasterize_small_f64   0.05764281   0.06274373   0.1286875   0.06520358   0.1128432   0.6000182    10
 fasterize_large_f64  36.91321005  37.71877265  41.0140303  40.81343803  43.9201820  46.5596799    10

Comparison with other tools

While rusterize is fast, there are other fast alternatives out there, including rasterio and geocube. However, rusterize allows for a seamless, Rust-native processing with similar or lower memory footprint that does not require you to install GDAL and returns the geoinformation you need for downstream processing with ample control over resolution, shape, extent, and data type.

The following is a time comparison of 10 runs (median) on the same large water bodies dataset used earlier (dtype is float64) (run_others.py).

rusterize: 1.3 sec
rasterio:  14.5 sec
geocube:   124.9 sec

Integrations

rusterize is integrated into the following libraries:

• rasterix

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