ee.Image.sample

Samples the pixels of an image, returning them as a FeatureCollection. Each feature will have 1 property per band in the input image. Note that the default behavior is to drop features that intersect masked pixels, which result in null-valued properties (see dropNulls argument).

Usage	Returns
Image.sample(region, scale, projection, factor, numPixels, seed, dropNulls, tileScale, geometries)	FeatureCollection

Argument	Type	Details
this: image	Image	The image to sample.
region	Geometry, default: null	The region to sample from. If unspecified, uses the image's whole footprint.
scale	Float, default: null	A nominal scale in meters of the projection to sample in.
projection	Projection, default: null	The projection in which to sample. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale.
factor	Float, default: null	A subsampling factor, within (0, 1]. If specified, 'numPixels' must not be specified. Defaults to no subsampling.
numPixels	Long, default: null	The approximate number of pixels to sample. If specified, 'factor' must not be specified.
seed	Integer, default: 0	A randomization seed to use for subsampling.
dropNulls	Boolean, default: true	Post filter the result to drop features that have null-valued properties.
tileScale	Float, default: 1	A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default.
geometries	Boolean, default: false	If true, adds the center of the sampled pixel as the geometry property of the output feature. Otherwise, geometries will be omitted (saving memory).

Examples

Code Editor (JavaScript)

// Demonstrate extracting pixels from an image as features with
// ee.Image.sample(), and show how the features are aligned with the pixels.

// An image with one band of elevation data.
var image = ee.Image('CGIAR/SRTM90_V4');
var VIS_MIN = 1620;
var VIS_MAX = 1650;
Map.addLayer(image, {min: VIS_MIN, max: VIS_MAX}, 'SRTM');

// Region to sample.
var region = ee.Geometry.Polygon(
  [[[-110.006, 40.002],
    [-110.006, 39.999],
    [-109.995, 39.999],
    [-109.995, 40.002]]], null, false);
// Show region on the map.
Map.setCenter(-110, 40, 16);
Map.addLayer(ee.FeatureCollection([region]).style({"color": "00FF0022"}));

// Perform sampling; convert image pixels to features.
var samples = image.sample({
  region: region,

  // Default (false) is no geometries in the output.
  // When set to true, each feature has a Point geometry at the center of the
  // image pixel.
  geometries: true,

  // The scale is not specified, so the resolution of the image will be used,
  // and there is a feature for every pixel. If we give a scale parameter, the
  // image will be resampled and there will be more or fewer features.
  //
  // scale: 200,
});

// Visualize sample data using ee.FeatureCollection.style().
var styled = samples
  .map(function (feature) {
    return feature.set('style', {
      pointSize: feature.getNumber('elevation').unitScale(VIS_MIN, VIS_MAX)
          .multiply(15),
    });
  })
  .style({
    color: '000000FF',
    fillColor: '00000000',
    styleProperty: 'style',
    neighborhood: 6,  // increase to correctly draw large points
  });
Map.addLayer(styled);

// Each sample feature has a point geometry and a property named 'elevation'
// corresponding to the band named 'elevation' of the image. If there are
// multiple bands they will become multiple properties. This will print:
//
// geometry: Point (-110.01, 40.00)
// properties:
//   elevation: 1639
print(samples.first());

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

import ee
import geemap.core as geemap

Colab (Python)

# Demonstrate extracting pixels from an image as features with
# ee.Image.sample(), and show how the features are aligned with the pixels.

# An image with one band of elevation data.
image = ee.Image('CGIAR/SRTM90_V4')
vis_min = 1620
vis_max = 1650
m = geemap.Map()
m.add_layer(image, {'min': vis_min, 'max': vis_max}, 'SRTM')

# Region to sample.
region = ee.Geometry.Polygon(
    [[
        [-110.006, 40.002],
        [-110.006, 39.999],
        [-109.995, 39.999],
        [-109.995, 40.002],
    ]],
    None,
    False,
)
# Show region on the map.
m.set_center(-110, 40, 16)

m.add_layer(ee.FeatureCollection([region]).style(color='00FF0022'))

# Perform sampling convert image pixels to features.
samples = image.sample(
    region=region,
    # Default (False) is no geometries in the output.
    # When set to True, each feature has a Point geometry at the center of the
    # image pixel.
    geometries=True,
    # The scale is not specified, so the resolution of the image will be used,
    # and there is a feature for every pixel. If we give a scale parameter, the
    # image will be resampled and there will be more or fewer features.
    #
    # scale=200,
)


def scale_point_size(feature):
  elevation = feature.getNumber('elevation')
  point_size = elevation.unitScale(vis_min, vis_max).multiply(15)
  feature.set('style', {'pointSize': point_size})
  return feature


# Visualize sample data using ee.FeatureCollection.style().
styled = samples.map(scale_point_size).style(
    color='000000FF',
    fillColor='00000000',
    styleProperty='style',
    neighborhood=6,  # increase to correctly draw large points
)
m.add_layer(styled)
display(m)

# Each sample feature has a point geometry and a property named 'elevation'
# corresponding to the band named 'elevation' of the image. If there are
# multiple bands they will become multiple properties. This will print:
#
# geometry: Point (-110.01, 40.00)
# properties:
#   elevation: 1639
display(samples.first())