ee.Classifier.smileRandomForest

Creates an empty Random Forest classifier.

Usage	Returns
ee.Classifier.smileRandomForest(numberOfTrees, variablesPerSplit, minLeafPopulation, bagFraction, maxNodes, seed)	Classifier

Argument	Type	Details
numberOfTrees	Integer	The number of decision trees to create.
variablesPerSplit	Integer, default: null	The number of variables per split. If unspecified, uses the square root of the number of variables.
minLeafPopulation	Integer, default: 1	Only create nodes whose training set contains at least this many points.
bagFraction	Float, default: 0.5	The fraction of input to bag per tree.
maxNodes	Integer, default: null	The maximum number of leaf nodes in each tree. If unspecified, defaults to no limit.
seed	Integer, default: 0	The randomization seed.

Examples

Code Editor (JavaScript)

// A Sentinel-2 surface reflectance image, reflectance bands selected,
// serves as the source for training and prediction in this contrived example.
var img = ee.Image('COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG')
              .select('B.*');

// ESA WorldCover land cover map, used as label source in classifier training.
var lc = ee.Image('ESA/WorldCover/v100/2020');

// Remap the land cover class values to a 0-based sequential series.
var classValues = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 100];
var remapValues = ee.List.sequence(0, 10);
var label = 'lc';
lc = lc.remap(classValues, remapValues).rename([label]);

// Add land cover as a band of the reflectance image and sample 500 pixels at
// 10 m scale from within a region of interest.
var roi = ee.Geometry.Rectangle(-122.347, 37.743, -122.024, 37.838);
var trainingSample = img.addBands(lc).sample({
  region: roi,
  scale: 10,
  numPixels: 500,
  geometries: true
});

// Train a 10-tree random forest classifier from the sample.
var trainedClassifier = ee.Classifier.smileRandomForest(10)
                            .train(trainingSample, label);

// Classify the reflectance image.
var classified = img.classify(trainedClassifier);

// Add the layers to the map.
var classVis = {
  min: 0,
  max: 10,
  palette: ['006400' ,'ffbb22', 'ffff4c', 'f096ff', 'fa0000', 'b4b4b4',
            'f0f0f0', '0064c8', '0096a0', '00cf75', 'fae6a0']
};
Map.setCenter(-122.184, 37.796, 12);
Map.addLayer(img, {bands: ['B11', 'B8', 'B3'], min: 100, max: 3500}, 'img');
Map.addLayer(lc, classVis, 'lc');
Map.addLayer(classified, classVis, 'classified');
Map.addLayer(roi, {color: 'white'}, 'roi', false, 0.5);
Map.addLayer(trainingSample, {color: 'black'}, 'trainingSample', false, 0.7);