Image Data Extraction

Getting image data from Earth Engine

To get image data from Earth Engine to Google Drive, Cloud Storage, or an Earth Engine asset, you can use Export and the job is handled entirely by Earth Engine. If your export jobs have scaling issues (e.g. take longer than a day, return memory or timeout errors) or you're already familiar with a framework like Apache Beam, Spark or Dask, you may prefer the data extraction methods described here. Workflows implemented in these frameworks can be scaled using Google Cloud tools such as Dataflow or Dataproc.

Specifically, this guide describes methods for manually making requests for image data using getPixels or computePixels. Here, "image data" means multi-dimensional arrays of pixel values with consistent scale and projection. The region, scale, projection and/or dimensions are specified in the request. The ImageFileFormat page lists possible output formats. Output destinations include Cloud Storage or any locally mounted directory. Manual requests add complexity, but can scale to larger workloads.

Getting image data from existing assets

Use getPixels to get image data from existing Earth Engine assets. You pass the asset ID directly to the request, so you can't do any computation on the pixels prior to extracting them. A block of pixels in the specified region, scale, projection and format is returned. The following example demonstrates getting time series of NDVI from a MODIS image collection using getPixels.

Getting image data from computed images

Use computePixels to get image data from a computed image, for example a composite. With computePixels, you pass a computed ee.Image object through the expression parameter. A block of computed pixels in the specified region, scale, projection and format is returned. The following example shows getting patches of multispectral data from a cloud-free Sentinel-2 composite.

Manual parallelization of requests

Though you can make requests for any purpose in any volume, you may want to parallelize requests for larger workflows. To make many such requests in parallel, you should use the Earth Engine high volume endpoint. The number of parallel requests you can have is set by your concurrent interactive request quota. See the Earth Engine high volume page for details on when to use the high volume endpoint.


You can use threads to make concurrrent requests. This approach is demonstrated in the getPixels and computePixels example notebooks.

Apache Beam

You can use Apache Beam pipelines to parallelize requests. These pipelines can be run locally or as Google Dataflow jobs. For examples, see this Geo for Good training or this People, Planet and AI demonstration. Alternatively, other parallelization libraries include Dask and Apache Spark.