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ee.FeatureCollection.cluster

  • TheFeatureCollection.cluster method clusters features in a collection and adds a new column with the assigned cluster number.

  • This method requires a FeatureCollection as input, a Clusterer, and an optional output name for the new property.

  • The output of the method is a FeatureCollection with the added cluster information.

 Clusters each feature in a collection, adding a new column to each feature containing the cluster number to which it has been assigned.

UsageReturns
FeatureCollection.cluster(clusterer,outputName)FeatureCollection
ArgumentTypeDetails
this:featuresFeatureCollectionThe collection of features to cluster. Each feature must contain all the properties in the clusterer's schema.
clustererClustererThe clusterer to use.
outputNameString, default: "cluster"The name of the output property to be added.

Examples

Code Editor (JavaScript)

// Import a Sentinel-2 surface reflectance image.varimage=ee.Image('COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG');// Get the image geometry to define the geographical bounds of a point sample.varimageBounds=image.geometry();// Sample the image at a set of random points; a feature collection is returned.varpointSampleFc=image.sample({region:imageBounds,scale:20,numPixels:1000,geometries:true});// Instantiate a k-means clusterer and train it.varclusterer=ee.Clusterer.wekaKMeans(5).train(pointSampleFc);// Cluster the input using the trained clusterer; optionally specify the name// of the output cluster ID property.varclusteredFc=pointSampleFc.cluster(clusterer,'spectral_cluster');print('Note added "spectral_cluster" property for an example feature',clusteredFc.first().toDictionary());// Visualize the clusters by applying a unique color to each cluster ID.varpalette=ee.List(['8dd3c7','ffffb3','bebada','fb8072','80b1d3']);varclusterVis=clusteredFc.map(function(feature){returnfeature.set('style',{color:palette.get(feature.get('spectral_cluster')),});}).style({styleProperty:'style'});// Display the points colored by cluster ID with the S2 image.Map.setCenter(-122.35,37.47,9);Map.addLayer(image,{bands:['B4','B3','B2'],min:0,max:1500},'S2 image');Map.addLayer(clusterVis,null,'Clusters');

Python setup

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

importeeimportgeemap.coreasgeemap

Colab (Python)

# Import a Sentinel-2 surface reflectance image.image=ee.Image('COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG')# Get the image geometry to define the geographical bounds of a point sample.image_bounds=image.geometry()# Sample the image at a set of random points a feature collection is returned.point_sample_fc=image.sample(region=image_bounds,scale=20,numPixels=1000,geometries=True)# Instantiate a k-means clusterer and train it.clusterer=ee.Clusterer.wekaKMeans(5).train(point_sample_fc)# Cluster the input using the trained clusterer optionally specify the name# of the output cluster ID property.clustered_fc=point_sample_fc.cluster(clusterer,'spectral_cluster')display('Note added "spectral_cluster" property for an example feature',clustered_fc.first().toDictionary(),)# Visualize the clusters by applying a unique color to each cluster ID.palette=ee.List(['8dd3c7','ffffb3','bebada','fb8072','80b1d3'])cluster_vis=clustered_fc.map(lambdafeature:feature.set('style',{'color':palette.get(feature.get('spectral_cluster'))})).style(styleProperty='style')# Display the points colored by cluster ID with the S2 image.m=geemap.Map()m.set_center(-122.35,37.47,9)m.add_layer(image,{'bands':['B4','B3','B2'],'min':0,'max':1500},'S2 image')m.add_layer(cluster_vis,None,'Clusters')m

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Last updated 2023-10-06 UTC.