ee.Classifier.train

    Trains the classifier on a collection of features, using the specified numeric properties of each feature as training data. The geometry of the features is ignored.
    Usage Returns
    Classifier. train (features, classProperty, inputProperties , subsampling , subsamplingSeed ) Classifier
    Argument Type Details
    this: classifier
    		Classifier An input classifier.
    features
    		FeatureCollection The collection to train on.
    classProperty
    		String The name of the property containing the class value. Each feature must have this property and its value must be numeric.
    inputProperties
    		List, default: null The list of property names to include as training data. Each feature must have all these properties and their values must be numeric. This argument is optional if the input collection contains a 'band_order' property, (as produced by Image.sample).
    subsampling
    		Float, default: 1 An optional subsampling factor, within (0, 1].
    subsamplingSeed
    		Integer, default: 0 A randomization seed to use for subsampling.

    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 
 ). 
 toByte 
 (); 
 // Add land cover as a band of the reflectance image and sample 100 pixels at 
 // 10 m scale from each land cover class within a region of interest. 
 var 
  
 roi 
  
 = 
  
 ee 
 . 
 Geometry 
 . 
 Rectangle 
 ( 
 - 
 122.347 
 , 
  
 37.743 
 , 
  
 - 
 122.024 
 , 
  
 37.838 
 ); 
 var 
  
 sample 
  
 = 
  
 img 
 . 
 addBands 
 ( 
 lc 
 ). 
 stratifiedSample 
 ({ 
  
 numPoints 
 : 
  
 100 
 , 
  
 classBand 
 : 
  
 label 
 , 
  
 region 
 : 
  
 roi 
 , 
  
 scale 
 : 
  
 10 
 , 
  
 geometries 
 : 
  
 true 
 }); 
 // Add a random value field to the sample and use it to approximately split 80% 
 // of the features into a training set and 20% into a validation set. 
 sample 
  
 = 
  
 sample 
 . 
 randomColumn 
 (); 
 var 
  
 trainingSample 
  
 = 
  
 sample 
 . 
 filter 
 ( 
 'random <= 0.8' 
 ); 
 var 
  
 validationSample 
  
 = 
  
 sample 
 . 
 filter 
 ( 
 'random > 0.8' 
 ); 
 // Train a 10-tree random forest classifier from the training sample. 
 var 
  
 trainedClassifier 
  
 = 
  
 ee 
 . 
 Classifier 
 . 
 smileRandomForest 
 ( 
 10 
 ). 
 train 
 ({ 
  
 features 
 : 
  
 trainingSample 
 , 
  
 classProperty 
 : 
  
 label 
 , 
  
 inputProperties 
 : 
  
 img 
 . 
 bandNames 
 () 
 }); 
 // Get information about the trained classifier. 
 print 
 ( 
 'Results of trained classifier' 
 , 
  
 trainedClassifier 
 . 
 explain 
 ()); 
 // Get a confusion matrix and overall accuracy for the training sample. 
 var 
  
 trainAccuracy 
  
 = 
  
 trainedClassifier 
 . 
 confusionMatrix 
 (); 
 print 
 ( 
 'Training error matrix' 
 , 
  
 trainAccuracy 
 ); 
 print 
 ( 
 'Training overall accuracy' 
 , 
  
 trainAccuracy 
 . 
 accuracy 
 ()); 
 // Get a confusion matrix and overall accuracy for the validation sample. 
 validationSample 
  
 = 
  
 validationSample 
 . 
 classify 
 ( 
 trainedClassifier 
 ); 
 var 
  
 validationAccuracy 
  
 = 
  
 validationSample 
 . 
 errorMatrix 
 ( 
 label 
 , 
  
 'classification' 
 ); 
 print 
 ( 
 'Validation error matrix' 
 , 
  
 validationAccuracy 
 ); 
 print 
 ( 
 'Validation accuracy' 
 , 
  
 validationAccuracy 
 . 
 accuracy 
 ()); 
 // Classify the reflectance image from the trained classifier. 
 var 
  
 imgClassified 
  
 = 
  
 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 
 ( 
 imgClassified 
 , 
  
 classVis 
 , 
  
 'Classified' 
 ); 
 Map 
 . 
 addLayer 
 ( 
 roi 
 , 
  
 { 
 color 
 : 
  
 'white' 
 }, 
  
 'ROI' 
 , 
  
 false 
 , 
  
 0.5 
 ); 
 Map 
 . 
 addLayer 
 ( 
 trainingSample 
 , 
  
 { 
 color 
 : 
  
 'black' 
 }, 
  
 'Training sample' 
 , 
  
 false 
 ); 
 Map 
 . 
 addLayer 
 ( 
 validationSample 
 , 
  
 { 
 color 
 : 
  
 'white' 
 }, 
  
 'Validation sample' 
 , 
  
 false 
 );

    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) 

     # A Sentinel-2 surface reflectance image, reflectance bands selected, 
 # serves as the source for training and prediction in this contrived example. 
 img 
 = 
 ee 
 . 
 Image 
 ( 
 'COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG' 
 ) 
 . 
 select 
 ( 
 'B.*' 
 ) 
 # ESA WorldCover land cover map, used as label source in classifier training. 
 lc 
 = 
 ee 
 . 
 Image 
 ( 
 'ESA/WorldCover/v100/2020' 
 ) 
 # Remap the land cover class values to a 0-based sequential series. 
 class_values 
 = 
 [ 
 10 
 , 
 20 
 , 
 30 
 , 
 40 
 , 
 50 
 , 
 60 
 , 
 70 
 , 
 80 
 , 
 90 
 , 
 95 
 , 
 100 
 ] 
 remap_values 
 = 
 ee 
 . 
 List 
 . 
 sequence 
 ( 
 0 
 , 
 10 
 ) 
 label 
 = 
 'lc' 
 lc 
 = 
 lc 
 . 
 remap 
 ( 
 class_values 
 , 
 remap_values 
 ) 
 . 
 rename 
 ( 
 label 
 ) 
 . 
 toByte 
 () 
 # Add land cover as a band of the reflectance image and sample 100 pixels at 
 # 10 m scale from each land cover class within a region of interest. 
 roi 
 = 
 ee 
 . 
 Geometry 
 . 
 Rectangle 
 ( 
 - 
 122.347 
 , 
 37.743 
 , 
 - 
 122.024 
 , 
 37.838 
 ) 
 sample 
 = 
 img 
 . 
 addBands 
 ( 
 lc 
 ) 
 . 
 stratifiedSample 
 ( 
 numPoints 
 = 
 100 
 , 
 classBand 
 = 
 label 
 , 
 region 
 = 
 roi 
 , 
 scale 
 = 
 10 
 , 
 geometries 
 = 
 True 
 ) 
 # Add a random value field to the sample and use it to approximately split 80% 
 # of the features into a training set and 20% into a validation set. 
 sample 
 = 
 sample 
 . 
 randomColumn 
 () 
 training_sample 
 = 
 sample 
 . 
 filter 
 ( 
 'random <= 0.8' 
 ) 
 validation_sample 
 = 
 sample 
 . 
 filter 
 ( 
 'random > 0.8' 
 ) 
 # Train a 10-tree random forest classifier from the training sample. 
 trained_classifier 
 = 
 ee 
 . 
 Classifier 
 . 
 smileRandomForest 
 ( 
 10 
 ) 
 . 
 train 
 ( 
 features 
 = 
 training_sample 
 , 
 classProperty 
 = 
 label 
 , 
 inputProperties 
 = 
 img 
 . 
 bandNames 
 (), 
 ) 
 # Get information about the trained classifier. 
 display 
 ( 
 'Results of trained classifier' 
 , 
 trained_classifier 
 . 
 explain 
 ()) 
 # Get a confusion matrix and overall accuracy for the training sample. 
 train_accuracy 
 = 
 trained_classifier 
 . 
 confusionMatrix 
 () 
 display 
 ( 
 'Training error matrix' 
 , 
 train_accuracy 
 ) 
 display 
 ( 
 'Training overall accuracy' 
 , 
 train_accuracy 
 . 
 accuracy 
 ()) 
 # Get a confusion matrix and overall accuracy for the validation sample. 
 validation_sample 
 = 
 validation_sample 
 . 
 classify 
 ( 
 trained_classifier 
 ) 
 validation_accuracy 
 = 
 validation_sample 
 . 
 errorMatrix 
 ( 
 label 
 , 
 'classification' 
 ) 
 display 
 ( 
 'Validation error matrix' 
 , 
 validation_accuracy 
 ) 
 display 
 ( 
 'Validation accuracy' 
 , 
 validation_accuracy 
 . 
 accuracy 
 ()) 
 # Classify the reflectance image from the trained classifier. 
 img_classified 
 = 
 img 
 . 
 classify 
 ( 
 trained_classifier 
 ) 
 # Add the layers to the map. 
 class_vis 
 = 
 { 
 'min' 
 : 
 0 
 , 
 'max' 
 : 
 10 
 , 
 'palette' 
 : 
 [ 
 '006400' 
 , 
 'ffbb22' 
 , 
 'ffff4c' 
 , 
 'f096ff' 
 , 
 'fa0000' 
 , 
 'b4b4b4' 
 , 
 'f0f0f0' 
 , 
 '0064c8' 
 , 
 '0096a0' 
 , 
 '00cf75' 
 , 
 'fae6a0' 
 , 
 ], 
 } 
 m 
 = 
 geemap 
 . 
 Map 
 () 
 m 
 . 
 set_center 
 ( 
 - 
 122.184 
 , 
 37.796 
 , 
 12 
 ) 
 m 
 . 
 add_layer 
 ( 
 img 
 , 
 { 
 'bands' 
 : 
 [ 
 'B11' 
 , 
 'B8' 
 , 
 'B3' 
 ], 
 'min' 
 : 
 100 
 , 
 'max' 
 : 
 3500 
 }, 
 'img' 
 ) 
 m 
 . 
 add_layer 
 ( 
 lc 
 , 
 class_vis 
 , 
 'lc' 
 ) 
 m 
 . 
 add_layer 
 ( 
 img_classified 
 , 
 class_vis 
 , 
 'Classified' 
 ) 
 m 
 . 
 add_layer 
 ( 
 roi 
 , 
 { 
 'color' 
 : 
 'white' 
 }, 
 'ROI' 
 , 
 False 
 , 
 0.5 
 ) 
 m 
 . 
 add_layer 
 ( 
 training_sample 
 , 
 { 
 'color' 
 : 
 'black' 
 }, 
 'Training sample' 
 , 
 False 
 ) 
 m 
 . 
 add_layer 
 ( 
 validation_sample 
 , 
 { 
 'color' 
 : 
 'white' 
 }, 
 'Validation sample' 
 , 
 False 
 ) 
 m
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