ee.Image.stratifiedSample

AI-generated Key Takeaways

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• Extracts a stratified random sample of points from an image based on distinct class values within a specified band.

• Returns a FeatureCollection containing one feature per sampled point, each with properties corresponding to the image bands.

• Allows customization of the number of samples per class, sampling region, scale, projection, and randomization.

• Handles cases with insufficient samples for a class by including all available points for that class.

• Requires the class band to contain integer values for proper stratification.

Extracts a stratified random sample of points from an image. Extracts the specified number of samples for each distinct value discovered within the 'classBand'. Returns a FeatureCollection of 1 Feature per extracted point, with each feature having 1 property per band in the input image. If there are less than the specified number of samples available for a given class value, then all of the points for that class will be included. Requires that the classBand contain integer values.

Usage	Returns
Image.stratifiedSample(numPoints, classBand, region, scale, projection, seed, classValues, classPoints, dropNulls, tileScale, geometries)	FeatureCollection

Argument	Type	Details
this: image	Image	The image to sample.
numPoints	Integer	The default number of points to sample in each class. Can be overridden for specific classes using the 'classValues' and 'classPoints' properties.
classBand	String, default: null	The name of the band containing the classes to use for stratification. If unspecified, the first band of the input image is used.
region	Geometry, default: null	The region to sample from. If unspecified, the input image's whole footprint is used.
scale	Float, default: null	A nominal scale in meters of the projection to sample in. Defaults to the scale of the first band of the input image.
projection	Projection, default: null	The projection in which to sample. If unspecified, the projection of the input image's first band is used. If specified in addition to scale, rescaled to the specified scale.
seed	Integer, default: 0	A randomization seed to use for subsampling.
classValues	List, default: null	A list of class values for which to override the numPoints parameter. Must be the same size as classPoints or null.
classPoints	List, default: null	A list of the per-class maximum number of pixels to sample for each class in the classValues list. Must be the same size as classValues or null.
dropNulls	Boolean, default: true	Skip pixels in which any band is masked.
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, the results will include a geometry per sampled pixel. Otherwise, geometries will be omitted (saving memory).