I would like to get the numpy arrays of some Google Earth Engine collection.

For example here my intent would be to obtain a 4d numpy array with dimensions [timestamp, height, width, bands] (or whatever order) with the Sigma0 data of a given region, defined by its bounds (here in UTM projection).


import ee
import geopandas as gpd
from shapely.geometry import box


crs = {'init': 'epsg:32628'}
poly = gpd.GeoSeries(box(578150.0, 1572350.0, 578350.0, 1572550.0), crs=crs)

poly_json = eval(poly.to_json())
gj_geom = poly_json['features'][0]['geometry'] 
geom = ee.Geometry(gj_geom, poly.crs['init'][5:])

collection = (ee.ImageCollection('COPERNICUS/S1_GRD')
              .filter(ee.Filter.date('2019-01-01', '2019-02-1'))

How do I obtain the raw data arrays from the filtered collection?


2 Answers 2


You can use ee.Image.sampleRectangle() (see this post) for converting GEE images to 2d array. However, there is a limit of 262144 pixels that can be transferred to protect your system from becoming unresponsive.

So for your collection, you can map over a function that applies this sampleRectangle and retrieves the respective array.


It's possible to export google earth engine image to local files using geemap library.

import geemap
import numpy as np

band_names = image.bandNames().getInfo() # List with band names
selected_band = image.select(band_name) # selected band to trnasform
image_array = geemap.ee_to_numpy(selected_band, region=geometry) # Client side
ndvi_array = np.squeeze(ndvi_array) # Deleting single-dimensional axis

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