ee.Classifier.smileNaiveBayes

 // 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 Naive Bayes classifier from the training sample. 
 var 
  
 trainedClassifier 
  
 = 
  
 ee 
 . 
 Classifier 
 . 
 smileNaiveBayes 
 (). 
 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 
 ); 

 # 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 Naive Bayes classifier from the training sample. 
 trained_classifier 
 = 
 ee 
 . 
 Classifier 
 . 
 smileNaiveBayes 
 () 
 . 
 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