/** * 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).varspectral=ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_038032_20160820').select('SR_B[1-7]');varlandcover=ee.Image('USGS/NLCD_RELEASES/2016_REL/2016').select('landcover');varsampleSource=spectral.addBands(landcover);// Sample the combined images to generate a FeatureCollection.varsample=sampleSource.sample({region:spectral.geometry(),// sample only from within Landsat image extentscale: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.vartraining=sample.filter('random < 0.8');print('Training set',training);// Train a random forest classifier.varclassifier=ee.Classifier.smileRandomForest(10).train({features:training,classProperty:landcover.bandNames().get(0),inputProperties:spectral.bandNames()});// Classify the sample.varpredictions=sample.classify({classifier:classifier,outputName:'predicted_landcover'});print('Predictions',predictions);// Split out the validation feature set.varvalidation=predictions.filter('random >= 0.8');print('Validation set',validation);// Get a list of possible class values to use for error matrix axis labels.varorder=sample.aggregate_array('landcover').distinct().sort();print('Error matrix axis labels',order);// Compute an error matrix that compares predicted vs. expected values.varerrorMatrix=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());

# 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'})display('Sample for classifier development:',sample)# Split out ~80% of the sample for training the classifier.training=sample.filter('random < 0.8')display('Training set:',training)# 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'})display('Predictions:',predictions)# Split out the validation feature set.validation=predictions.filter('random >= 0.8')display('Validation set:',validation)# Get a list of possible class values to use for error matrix axis labels.order=sample.aggregate_array('landcover').distinct().sort()display('Error matrix axis labels:',order)# Compute an error matrix that compares predicted vs. expected values.error_matrix=validation.errorMatrix(**{'actual':landcover.bandNames().get(0),'predicted':'predicted_landcover','order':order})display('Error matrix:',error_matrix)# Compute accuracy metrics from the error matrix.display('Overall accuracy:',error_matrix.accuracy())display('Consumer\'s accuracy:',error_matrix.consumersAccuracy())display('Producer\'s accuracy:',error_matrix.producersAccuracy())display('Kappa:',error_matrix.kappa())