What is GeoPySpark?

GeoPySpark is a python language binding library of the scala library, GeoTrellis. Like GeoTrellis, this project is released under the Apache 2 License.

GeoPySpark seeks to utilize GeoTrellis to allow for the reading, writing, and operating on raster data. Thus, its able to scale to the data and still be able to perform well.

In addition to raster processing, GeoPySpark allows for rasters to be rendered into PNGs. One of the goals of this project to be able to process rasters at web speeds and to perform batch processing of large data sets.

Why GeoPySpark?

Raster processing in Python has come a long way; however, issues still arise as the size of the dataset increases. Whether it is performance or ease of use, these sorts of problems will become more common as larger amounts of data are made available to the public.

One could turn to GeoTrellis to resolve the aforementioned problems (and one should try it out!), yet this brings about new challanges. Scala, while a powerful language, has something of a steep learning curve. This can put off those who do not have the time and/or interest in learning a new language.

By having the speed and scalability of Scala and the ease of Python, GeoPySpark is then the remedy to this predicament.

A Quick Example

Here is a quick example of GeoPySpark. In the following code, we take NLCD data of the state of Pennslyvania from 2011, and do a polygonal summary of an area of interest to find the min and max classifcations values of that area.

If you wish to follow along with this example, you will need to download the NLCD data and the geojson that represents the area of interest. Running these two commands will download these files for you:

curl -o /tmp/NLCD2011_LC_Pennsylvannia.zip https://s3-us-west-2.amazonaws.com/prd-tnm/StagedProducts/NLCD/2011/landcover/states/NLCD2011_LC_Pennsylvania.zip?ORIG=513_SBDDG
unzip /tmp/NLCD2011_LC_Pennsylvannia.zip
curl -o /tmp/area_of_interest.geojson https://s3.amazonaws.com/geopyspark-test/area_of_interest.json
import json
from functools import partial

from geopyspark.geopycontext import GeoPyContext
from geopyspark.geotrellis.constants import SPATIAL, ZOOM
from geopyspark.geotrellis.geotiff_rdd import get
from geopyspark.geotrellis.catalog import write

from shapely.geometry import Polygon, shape
from shapely.ops import transform
import pyproj

# Create the GeoPyContext
geopysc = GeoPyContext(appName="example", master="local[*]")

# Read in the NLCD tif that has been saved locally.
# This tif represents the state of Pennsylvania.
raster_rdd = get(geopysc=geopysc, rdd_type=SPATIAL,
options={'numPartitions': 100})

tiled_rdd = raster_rdd.to_tiled_layer()

# Reproject the reclassified TiledRasterRDD so that it is in WebMercator
reprojected_rdd = tiled_rdd.reproject(3857, scheme=ZOOM).cache().repartition(150)

# We will do a polygonal summary of the north-west region of Philadelphia.
with open('/tmp/area_of_interest.json') as f:
    txt = json.load(f)

geom = shape(txt['features'][0]['geometry'])

# We need to reporject the geometry to WebMercator so that it will intersect with
# the TiledRasterRDD.
project = partial(

area_of_interest = transform(project, geom)

# Find the min and max of the values within the area of interest polygon.
min_val = reprojected_rdd.polygonal_min(geometry=area_of_interest, data_type=int)
max_val = reprojected_rdd.polygonal_max(geometry=area_of_interest, data_type=int)

print('The min value of the area of interest is:', min_val)
print('The max value of the area of interest is:', max_val)

# We will now pyramid the relcassified TiledRasterRDD so that we can use it in a TMS server later.
pyramided_rdd = reprojected_rdd.pyramid(start_zoom=1, end_zoom=12)

# Save each layer of the pyramid locally so that it can be accessed at a later time.
for pyramid in pyramided_rdd:
    write('file:///tmp/nld-2011', 'pa', pyramid)

Contact and Support

If you need help, have questions, or like to talk to the developers (let us know what you’re working on!) you contact us at:

As you may have noticed from the above links, those are links to the GeoTrellis gitter channel and mailing list. This is because this project is currently an offshoot of GeoTrellis, and we will be using their mailing list and gitter channel as a means of contact. However, we will form our own if there is a need for it.