geobeam

geobeam adds GIS capabilities to your Apache Beam pipelines.

What does geobeam do?

geobeam enables you to ingest and analyze massive amounts of geospatial data in parallel using Dataflow. geobeam provides a set of FileBasedSource classes that make it easy to read, process, and write geospatial data, and provides a set of helpful Apache Beam transforms and utilities that make it easier to process GIS data in your Dataflow pipelines.

See the Full Documentation for complete API specification.

Requirements

• Apache Beam 2.46+
• Python 3.8+

Note: Make sure the Python version used to run the pipeline matches the version in the built container.

Supported input types

File format	Data type	Geobeam class
tiff	raster	RasterBlockSource and RasterPolygonSource
shp	vector	ShapefileSource
gdb	vector	GeodatabaseSource
json	vector	GeoJSONSource
URL	vector	ESRIServerSource

Included libraries

geobeam includes several python modules that allow you to perform a wide variety of operations and analyses on your geospatial data.

Module	Version	Description
gdal	3.5.2	python bindings for GDAL
rasterio	1.3.2	reads and writes geospatial raster data
fiona	1.8.21	reads and writes geospatial vector data
shapely	1.8.4	manipulation and analysis of geometric objects in the cartesian plane
esridump	1.11.0	read layer from ESRI server

How to Use

1. Install the module

pip install geobeam

2. Write your pipeline

Write a normal Apache Beam pipeline using one of geobeams file sources. See geobeam/examples for inspiration.

3. Run

Run locally

python -m geobeam.examples.geotiff_dem \
  --gcs_url gs://geobeam/examples/dem-clipped-test.tif \
  --dataset examples \
  --table dem \
  --band_column elev \
  --runner DirectRunner \
  --temp_location <temp gs://> \
  --project <project_id>

Note: Some of the provided examples may take a very long time to run locally...

Run in Dataflow

Write a Dockerfile

This will run in Dataflow as a custom container based on the dataflow-geobeam/base image. It is recommended that you publish your own container based on the Dockerfile in this repository and store it in your project's GCR registry.

FROM gcr.io/dataflow-geobeam/base

RUN pip install geobeam

COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

# build locally with docker
docker build -t gcr.io/<project_id>/geobeam
docker push gcr.io/<project_id>/geobeam

# or build with Cloud Build
gcloud builds submit --timeout 3600s --worker_machine_type n1-highcpu-8

Start the Dataflow job

# run the geotiff_soilgrid example in dataflow
python -m geobeam.examples.geotiff_soilgrid \
  --gcs_url gs://geobeam/examples/AWCh3_M_sl1_250m_ll.tif \
  --dataset examples \
  --table soilgrid \
  --band_column h3 \
  --runner DataflowRunner \
  --sdk_container_image gcr.io/dataflow-geobeam/base \
  --temp_location <temp bucket> \
  --service_account_email <service account> \
  --region us-central1 \
  --max_num_workers 2 \
  --worker_machine_type c2-standard-30 \

Examples

Read Raster as Blocks

def run(options):
  from geobeam.io import RasterBlockSource
  from geobeam.fn import format_rasterblock_record

  with beam.Pipeline(options) as p:
    (p  | 'ReadRaster' >> beam.io.Read(RasterBlockSource(gcs_url))
        | 'FormatRecord' >> beam.Map(format_rasterblock_record)
        | 'WriteToBigquery' >> beam.io.WriteToBigQuery('geo.dem'))

Validate and Simplify Shapefile

def run(options):
  from geobeam.io import ShapefileSource
  from geobeam.fn import make_valid, filter_invalid, format_record

  with beam.Pipeline(options) as p:
    (p  | 'ReadShapefile' >> beam.io.Read(ShapefileSource(gcs_url))
        | 'Validate' >> beam.Map(make_valid)
        | 'FilterInvalid' >> beam.Filter(filter_invalid)
        | 'FormatRecord' >> beam.Map(format_record)
        | 'WriteToBigquery' >> beam.io.WriteToBigQuery('geo.parcel'))

See geobeam/examples/ for complete examples.

A number of example pipelines are available in the geobeam/examples/ folder. To run them in your Google Cloud project, run the included terraform file to set up the Bigquery dataset and tables used by the example pipelines.

Open up Bigquery GeoViz to visualize your data.

Shapefile Example

The National Flood Hazard Layer loaded from a shapefile. Example pipeline at geobeam/examples/shapefile_nfhl.py

Raster Example

The Digital Elevation Model is a high-resolution model of elevation measurements at 1-meter resolution. (Values converted to centimeters). Example pipeline: geobeam/examples/geotiff_dem.py.

Included Transforms

The geobeam.fn module includes several Beam Transforms that you can use in your pipelines.

Module	Description
geobeam.fn.make_valid	Attempt to make all geometries valid.
geobeam.fn.filter_invalid	Filter out invalid geometries that cannot be made valid
geobeam.fn.format_record	Format the (props, geom) tuple received from a vector source into a dict that can be inserted into the destination table
geobeam.fn.format_rasterblock_record	Format the output record for blocks read from RasterBlockSource
geobeam.fn.format_rasterpolygon_record	Format the output record for blocks read from RasterPolygonSource

Execution parameters

Each FileSource accepts several parameters that you can use to configure how your data is loaded and processed. These can be parsed as pipeline arguments and passed into the respective FileSources as seen in the examples pipelines.

Parameter	Input type	Description	Default	Required?
skip_reproject	All	True to skip reprojection during read	False	No
in_epsg	All	An EPSG integer to override the input source CRS to reproject from		No
in_proj	All	A PROJ string to override the input source CRS		No
band_number	Raster	The raster band to read from	1	No
include_nodata	Raster	True to include nodata values	False	No
return_block_transform	Raster	True to include rasterio transform object with each block to use with geobeam.fn.format_rasterpixel_record	False	No
layer_name	Vector	Name of layer to read		Yes, for shapefiles
gdb_name	Vector	Name of geodatabase directory in a gdb zip archive		Yes, for GDB files

License

This is not an officially supported Google product, though support will be provided on a best-effort basis.

Copyright 2023 Google LLC

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    https://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.