University of California
Informatics and GIS Program

Cloud-based raster processors out there

Hi all,

Just trying to get my head around some of the new big raster processors out there, in addition of course to Google Earth Engine. Bear with me (bare?) while I sort through these. Thanks for raster sleuth Stefania Di Tomasso for the leg work. 

1. Geotrellis (https://geotrellis.io/)

Geotrellis is a Scala-based raster processing engine, and it is one of the first geospatial libraries on Spark.  Geotrellis is able to process big datasets. Users can interact with geospatial data and see results in real time in an interactive web application (for regional, statewide dataset).  For larger raster datasets (eg. US NED). GeoTrellis performs fast batch processing using Akka clustering to distribute data across the cluster.  GeoTrellis was designed to solve three core problems, with a focus on raster processing:

  • Creating scalable, high performance geoprocessing web services;
  • Creating distributed geoprocessing services that can act on large data sets; and
  • Parallelizing geoprocessing operations to take full advantage of multi-core architecture.

Features:

  • GeoTrellis is designed to help a developer create simple, standard REST services that return the results of geoprocessing models.
  • GeoTrellis will automatically parallelize and optimize your geoprocessing models where possible.
  • In the spirit of the object-functional style of Scala, it is easy to both create new operations and compose new operations with existing operations.

2. GeoPySpark - in synthesis GeoTrellis for Python community

Geopyspark provides python bindings for working with geospatial data on PySpark (PySpark is the Python API for Spark). Spark is open source processing engine originally developed at UC Berkeley in 2009.  GeoPySpark makes Geotrellis (https://geotrellis.io/) accessible to the python community.  Scala is a difficult language so they have created this Python library. 

3. RasterFoundry

Posted on Tuesday, May 2, 2017 at 5:58 PM
Tags: big (3), cloud (6), coding (10), raster (1)

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