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ee.FeatureCollection.errorMatrix

Computes a 2D error matrix for a collection by comparing two columns of a collection: one containing the actual values, and one containing predicted values. The values are expected to be small contiguous integers, starting from 0. Axis 0 (the rows) of the matrix correspond to the actual values, and Axis 1 (the columns) to the predicted values.

Usage	Returns
`FeatureCollection. errorMatrix (actual, predicted, order )`	ConfusionMatrix

Argument	Type	Details
this: `collection`	FeatureCollection	The input collection.
`actual`	String	The name of the property containing the actual value.
`predicted`	String	The name of the property containing the predicted value.
`order`	List, default: null	A list of the expected values. If this argument is not specified, the values are assumed to be contiguous and span the range 0 to maxValue. If specified, only values matching this list are used, and the matrix will have dimensions and order matching this list.

Examples

Code Editor (JavaScript)

 /** 
 * 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 
 ());

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)

 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:' 
 ) 
 pprint 
 ( 
 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 
 ())

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Examples

Code Editor (JavaScript)

Colab (Python)

ee.FeatureCollection.errorMatrix