I am trying to sample the Global Forest Change dataset using randomly drawn points and the reduceRegion function. The way I construct this is:

var sampledPoints = gfc2014_c.reduceRegions({
  reducer: ee.Reducer.mean()

Where gfc2014_c is the GFC datset cropped to sub-Saharan Africa and points are my randomly drawn points. The entire code is here.

I was aiming for a large sample of 1 million pixels. When I try to export in chunks (even chunks as small as 5,000) I get the following error:

Error: image.reduceRegions: Computed value is too large.

What am I doing wrong? I know the GFC dataset is really large, but it must be possible, right?

  • Interesting problem. I'll tinker with it over the weekend. In the meantime, try: 1) preping your point sample with all the joins etc and export it as an asset, then import it to do the region reduction; 2) don't clip the image data being reduced; 3) try mapping reduceRegion over the point sample instead of using reduceRegions (developers.google.com/earth-engine/…) Dec 28, 2019 at 0:44
  • Thank you Justin. I just tried your third suggestion, currently attempting an export of 100,000 observations. Fingers crossed!
    – C. Ashley
    Dec 28, 2019 at 17:51
  • What was the outcome - keep me posted. Dec 29, 2019 at 20:30
  • Mapping - reduce region sped up the process a lot. Thanks!
    – C. Ashley
    Dec 31, 2019 at 1:16
  • I'm glad that mapping reduceRegion() resulted in better performance! Dec 31, 2019 at 23:35

1 Answer 1


Based on Justin's comment above and the part of the debugging guide he directed me to, I tried the following:

var sampledPoints = points.map(function(feature) {
  return feature.set(gfc2014.reduceRegion({
    reducer: 'mean',
    geometry: feature.geometry(),

This seems to have worked: I can now export a chunk of 100,000 observations in 5 hours. Thank you Justin!

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