ee.FeatureCollection.classify

 /** 
 * Classifies features in a FeatureCollection and computes an error matrix. 
 */ 
 // Combine Landsat and NLCD images using only the bands representing 
 // predictor variables (spectral reflectance) and target labels (land cover). 
 var 
  
 spectral 
  
 = 
  
 ee 
 . 
 Image 
 ( 
 'LANDSAT/LC08/C02/T1_L2/LC08_038032_20160820' 
 ). 
 select 
 ( 
 'SR_B[1-7]' 
 ); 
 var 
  
 landcover 
  
 = 
  
 ee 
 . 
 Image 
 ( 
 'USGS/NLCD_RELEASES/2016_REL/2016' 
 ). 
 select 
 ( 
 'landcover' 
 ); 
 var 
  
 sampleSource 
  
 = 
  
 spectral 
 . 
 addBands 
 ( 
 landcover 
 ); 
 // Sample the combined images to generate a FeatureCollection. 
 var 
  
 sample 
  
 = 
  
 sampleSource 
 . 
 sample 
 ({ 
  
 region 
 : 
  
 spectral 
 . 
 geometry 
 (), 
  
 // sample only from within Landsat image extent 
  
 scale 
 : 
  
 30 
 , 
  
 numPixels 
 : 
  
 2000 
 , 
  
 geometries 
 : 
  
 true 
 }) 
 // Add a random value column with uniform distribution for hold-out 
 // training/validation splitting. 
 . 
 randomColumn 
 ({ 
 distribution 
 : 
  
 'uniform' 
 }); 
 print 
 ( 
 'Sample for classifier development' 
 , 
  
 sample 
 ); 
 // Split out ~80% of the sample for training the classifier. 
 var 
  
 training 
  
 = 
  
 sample 
 . 
 filter 
 ( 
 'random < 0.8' 
 ); 
 print 
 ( 
 'Training set' 
 , 
  
 training 
 ); 
 // Train a random forest classifier. 
 var 
  
 classifier 
  
 = 
  
 ee 
 . 
 Classifier 
 . 
 smileRandomForest 
 ( 
 10 
 ). 
 train 
 ({ 
  
 features 
 : 
  
 training 
 , 
  
 classProperty 
 : 
  
 landcover 
 . 
 bandNames 
 (). 
 get 
 ( 
 0 
 ), 
  
 inputProperties 
 : 
  
 spectral 
 . 
 bandNames 
 () 
 }); 
 // Classify the sample. 
 var 
  
 predictions 
  
 = 
  
 sample 
 . 
 classify 
 ( 
  
 { 
 classifier 
 : 
  
 classifier 
 , 
  
 outputName 
 : 
  
 'predicted_landcover' 
 }); 
 print 
 ( 
 'Predictions' 
 , 
  
 predictions 
 ); 
 // Split out the validation feature set. 
 var 
  
 validation 
  
 = 
  
 predictions 
 . 
 filter 
 ( 
 'random >= 0.8' 
 ); 
 print 
 ( 
 'Validation set' 
 , 
  
 validation 
 ); 
 // Get a list of possible class values to use for error matrix axis labels. 
 var 
  
 order 
  
 = 
  
 sample 
 . 
 aggregate_array 
 ( 
 'landcover' 
 ). 
 distinct 
 (). 
 sort 
 (); 
 print 
 ( 
 'Error matrix axis labels' 
 , 
  
 order 
 ); 
 // Compute an error matrix that compares predicted vs. expected values. 
 var 
  
 errorMatrix 
  
 = 
  
 validation 
 . 
 errorMatrix 
 ({ 
  
 actual 
 : 
  
 landcover 
 . 
 bandNames 
 (). 
 get 
 ( 
 0 
 ), 
  
 predicted 
 : 
  
 'predicted_landcover' 
 , 
  
 order 
 : 
  
 order 
 }); 
 print 
 ( 
 'Error matrix' 
 , 
  
 errorMatrix 
 ); 
 // Compute accuracy metrics from the error matrix. 
 print 
 ( 
 "Overall accuracy" 
 , 
  
 errorMatrix 
 . 
 accuracy 
 ()); 
 print 
 ( 
 "Consumer's accuracy" 
 , 
  
 errorMatrix 
 . 
 consumersAccuracy 
 ()); 
 print 
 ( 
 "Producer's accuracy" 
 , 
  
 errorMatrix 
 . 
 producersAccuracy 
 ()); 
 print 
 ( 
 "Kappa" 
 , 
  
 errorMatrix 
 . 
 kappa 
 ()); 

 from 
  
 pprint 
  
 import 
 pprint 
 # Classifies features in a FeatureCollection and computes an error matrix. 
 # Combine Landsat and NLCD images using only the bands representing 
 # predictor variables (spectral reflectance) and target labels (land cover). 
 spectral 
 = 
 ee 
 . 
 Image 
 ( 
 'LANDSAT/LC08/C02/T1_L2/LC08_038032_20160820' 
 ) 
 . 
 select 
 ( 
 'SR_B[1-7]' 
 ) 
 landcover 
 = 
 ee 
 . 
 Image 
 ( 
 'USGS/NLCD_RELEASES/2016_REL/2016' 
 ) 
 . 
 select 
 ( 
 'landcover' 
 ) 
 sample_source 
 = 
 spectral 
 . 
 addBands 
 ( 
 landcover 
 ) 
 # Sample the combined images to generate a FeatureCollection. 
 sample 
 = 
 sample_source 
 . 
 sample 
 ( 
 ** 
 { 
 # sample only from within Landsat image extent 
 'region' 
 : 
 spectral 
 . 
 geometry 
 (), 
 'scale' 
 : 
 30 
 , 
 'numPixels' 
 : 
 2000 
 , 
 'geometries' 
 : 
 True 
 }) 
 # Add a random value column with uniform distribution for hold-out 
 # training/validation splitting. 
 sample 
 = 
 sample 
 . 
 randomColumn 
 ( 
 ** 
 { 
 'distribution' 
 : 
 'uniform' 
 }) 
 print 
 ( 
 'Sample for classifier development:' 
 , 
 sample 
 . 
 getInfo 
 ()) 
 # Split out ~80% of the sample for training the classifier. 
 training 
 = 
 sample 
 . 
 filter 
 ( 
 'random < 0.8' 
 ) 
 print 
 ( 
 'Training set:' 
 , 
 training 
 . 
 getInfo 
 ()) 
 # Train a random forest classifier. 
 classifier 
 = 
 ee 
 . 
 Classifier 
 . 
 smileRandomForest 
 ( 
 10 
 ) 
 . 
 train 
 ( 
 ** 
 { 
 'features' 
 : 
 training 
 , 
 'classProperty' 
 : 
 landcover 
 . 
 bandNames 
 () 
 . 
 get 
 ( 
 0 
 ), 
 'inputProperties' 
 : 
 spectral 
 . 
 bandNames 
 () 
 }) 
 # Classify the sample. 
 predictions 
 = 
 sample 
 . 
 classify 
 ( 
 ** 
 { 
 'classifier' 
 : 
 classifier 
 , 
 'outputName' 
 : 
 'predicted_landcover' 
 }) 
 print 
 ( 
 'Predictions:' 
 , 
 predictions 
 . 
 getInfo 
 ()) 
 # Split out the validation feature set. 
 validation 
 = 
 predictions 
 . 
 filter 
 ( 
 'random >= 0.8' 
 ) 
 print 
 ( 
 'Validation set:' 
 , 
 validation 
 . 
 getInfo 
 ()) 
 # Get a list of possible class values to use for error matrix axis labels. 
 order 
 = 
 sample 
 . 
 aggregate_array 
 ( 
 'landcover' 
 ) 
 . 
 distinct 
 () 
 . 
 sort 
 () 
 print 
 ( 
 'Error matrix axis labels:' 
 , 
 order 
 . 
 getInfo 
 ()) 
 # Compute an error matrix that compares predicted vs. expected values. 
 error_matrix 
 = 
 validation 
 . 
 errorMatrix 
 ( 
 ** 
 { 
 'actual' 
 : 
 landcover 
 . 
 bandNames 
 () 
 . 
 get 
 ( 
 0 
 ), 
 'predicted' 
 : 
 'predicted_landcover' 
 , 
 'order' 
 : 
 order 
 }) 
 print 
 ( 
 'Error matrix:' 
 ) 
 pprint 
 ( 
 error_matrix 
 . 
 getInfo 
 ()) 
 # Compute accuracy metrics from the error matrix. 
 print 
 ( 
 'Overall accuracy:' 
 , 
 error_matrix 
 . 
 accuracy 
 () 
 . 
 getInfo 
 ()) 
 print 
 ( 
 'Consumer 
 \' 
 s accuracy:' 
 ) 
 pprint 
 ( 
 error_matrix 
 . 
 consumersAccuracy 
 () 
 . 
 getInfo 
 ()) 
 print 
 ( 
 'Producer 
 \' 
 s accuracy:' 
 ) 
 pprint 
 ( 
 error_matrix 
 . 
 producersAccuracy 
 () 
 . 
 getInfo 
 ()) 
 print 
 ( 
 'Kappa:' 
 , 
 error_matrix 
 . 
 kappa 
 () 
 . 
 getInfo 
 ())