Export.classifier.toAsset

 // First gather the training data for a random forest classifier. 
 // Let's use MCD12Q1 yearly landcover for the labels. 
 var 
  
 landcover 
  
 = 
  
 ee 
 . 
 ImageCollection 
 ( 
 'MODIS/061/MCD12Q1' 
 ) 
  
 . 
 filterDate 
 ( 
 '2022-01-01' 
 , 
  
 '2022-12-31' 
 ) 
  
 . 
 first 
 () 
  
 . 
 select 
 ( 
 'LC_Type1' 
 ); 
 // A region of interest for training our classifier. 
 var 
  
 region 
  
 = 
  
 ee 
 . 
 Geometry 
 . 
 BBox 
 ( 
 17.33 
 , 
  
 36.07 
 , 
  
 26.13 
 , 
  
 43.28 
 ); 
 // Training features will be based on a Landsat 8 composite. 
 var 
  
 l8 
  
 = 
  
 ee 
 . 
 ImageCollection 
 ( 
 'LANDSAT/LC08/C02/T1' 
 ) 
  
 . 
 filterBounds 
 ( 
 region 
 ) 
  
 . 
 filterDate 
 ( 
 '2022-01-01' 
 , 
  
 '2023-01-01' 
 ); 
 // Draw the Landsat composite, visualizing true color bands. 
 var 
  
 landsatComposite 
  
 = 
  
 ee 
 . 
 Algorithms 
 . 
 Landsat 
 . 
 simpleComposite 
 ({ 
  
 collection 
 : 
  
 l8 
 , 
  
 asFloat 
 : 
  
 true 
 }); 
 Map 
 . 
 addLayer 
 ( 
 landsatComposite 
 , 
  
 { 
  
 min 
 : 
  
 0 
 , 
  
 max 
 : 
  
 0.3 
 , 
  
 bands 
 : 
  
 [ 
 'B3' 
 , 
  
 'B2' 
 , 
  
 'B1' 
 ] 
 }, 
  
 'Landsat composite' 
 ); 
 // Make a training dataset by sampling the stacked images. 
 var 
  
 training 
  
 = 
  
 landcover 
 . 
 addBands 
 ( 
 landsatComposite 
 ). 
 sample 
 ({ 
  
 region 
 : 
  
 region 
 , 
  
 scale 
 : 
  
 30 
 , 
  
 // With export to Classifier we can bump this higher to say 10,000. 
  
 numPixels 
 : 
  
 1000 
 }); 
 var 
  
 classifier 
  
 = 
  
 ee 
 . 
 Classifier 
 . 
 smileRandomForest 
 ({ 
  
 // We can also increase the number of trees higher to ~100 if needed. 
  
 numberOfTrees 
 : 
  
 3 
 }). 
 train 
 ({ 
 features 
 : 
  
 training 
 , 
  
 classProperty 
 : 
  
 'LC_Type1' 
 }); 
 // Create an export classifier task to run. 
 var 
  
 assetId 
  
 = 
  
 'projects/<project-name>/assets/<asset-name>' 
 ; 
  
 // <> modify these 
 Export 
 . 
 classifier 
 . 
 toAsset 
 ({ 
  
 classifier 
 : 
  
 classifier 
 , 
  
 description 
 : 
  
 'classifier_export' 
 , 
  
 assetId 
 : 
  
 assetId 
 }); 
 // Load the classifier after the export finishes and visualize. 
 var 
  
 savedClassifier 
  
 = 
  
 ee 
 . 
 Classifier 
 . 
 load 
 ( 
 assetId 
 ) 
 var 
  
 landcoverPalette 
  
 = 
  
 '05450a,086a10,54a708,78d203,009900,c6b044,dcd159,' 
  
 + 
  
 'dade48,fbff13,b6ff05,27ff87,c24f44,a5a5a5,ff6d4c,69fff8,f9ffa4,1c0dff' 
 ; 
 var 
  
 landcoverVisualization 
  
 = 
  
 { 
  
 palette 
 : 
  
 landcoverPalette 
 , 
  
 min 
 : 
  
 0 
 , 
  
 max 
 : 
  
 16 
 , 
  
 format 
 : 
  
 'png' 
 }; 
 Map 
 . 
 addLayer 
 ( 
  
 landsatComposite 
 . 
 classify 
 ( 
 savedClassifier 
 ), 
  
 landcoverVisualization 
 , 
  
 'Upsampled landcover, saved' 
 ); 

 # First gather the training data for a random forest classifier. 
 # Let's use MCD12Q1 yearly landcover for the labels. 
 landcover 
 = 
 ( 
 ee 
 . 
 ImageCollection 
 ( 
 'MODIS/061/MCD12Q1' 
 ) 
 . 
 filterDate 
 ( 
 '2022-01-01' 
 , 
 '2022-12-31' 
 ) 
 . 
 first 
 () 
 . 
 select 
 ( 
 'LC_Type1' 
 )) 
 # A region of interest for training our classifier. 
 region 
 = 
 ee 
 . 
 Geometry 
 . 
 BBox 
 ( 
 17.33 
 , 
 36.07 
 , 
 26.13 
 , 
 43.28 
 ) 
 # Training features will be based on a Landsat 8 composite. 
 l8 
 = 
 ( 
 ee 
 . 
 ImageCollection 
 ( 
 'LANDSAT/LC08/C02/T1' 
 ) 
 . 
 filterBounds 
 ( 
 region 
 ) 
 . 
 filterDate 
 ( 
 '2022-01-01' 
 , 
 '2023-01-01' 
 )) 
 # Draw the Landsat composite, visualizing true color bands. 
 landsatComposite 
 = 
 ee 
 . 
 Algorithms 
 . 
 Landsat 
 . 
 simpleComposite 
 ( 
 collection 
 = 
 l8 
 , 
 asFloat 
 = 
 True 
 ) 
 Map 
 = 
 geemap 
 . 
 Map 
 () 
 Map 
 # Render the map in the notebook. 
 Map 
 . 
 addLayer 
 ( 
 landsatComposite 
 , 
 { 
 'min' 
 : 
 0 
 , 
 'max' 
 : 
 0.3 
 , 
 'bands' 
 : 
 [ 
 'B3' 
 , 
 'B2' 
 , 
 'B1' 
 ] 
 }, 
 'Landsat composite' 
 ) 
 # Make a training dataset by sampling the stacked images. 
 training 
 = 
 landcover 
 . 
 addBands 
 ( 
 landsatComposite 
 ) 
 . 
 sample 
 ( 
 region 
 = 
 region 
 , 
 scale 
 = 
 30 
 , 
 # With export to Classifier we can bump this higher to say 10,000. 
 numPixels 
 = 
 1000 
 ) 
 # We can also increase the number of trees higher to ~100 if needed. 
 classifier 
 = 
 ee 
 . 
 Classifier 
 . 
 smileRandomForest 
 ( 
 numberOfTrees 
 = 
 3 
 ) 
 . 
 train 
 ( 
 features 
 = 
 training 
 , 
 classProperty 
 = 
 'LC_Type1' 
 ) 
 # Create an export classifier task to run. 
 asset_id 
 = 
 'projects/<project-name>/assets/<asset-name>' 
 # <> modify these 
 ee 
 . 
 batch 
 . 
 Export 
 . 
 classifier 
 . 
 toAsset 
 ( 
 classifier 
 = 
 classifier 
 , 
 description 
 = 
 'classifier_export' 
 , 
 assetId 
 = 
 asset_id 
 ) 
 # Load the classifier after the export finishes and visualize. 
 savedClassifier 
 = 
 ee 
 . 
 Classifier 
 . 
 load 
 ( 
 asset_id 
 ) 
 landcover_palette 
 = 
 [ 
 '05450a' 
 , 
 '086a10' 
 , 
 '54a708' 
 , 
 '78d203' 
 , 
 '009900' 
 , 
 'c6b044' 
 , 
 'dcd159' 
 , 
 'dade48' 
 , 
 'fbff13' 
 , 
 'b6ff05' 
 , 
 '27ff87' 
 , 
 'c24f44' 
 , 
 'a5a5a5' 
 , 
 'ff6d4c' 
 , 
 '69fff8' 
 , 
 'f9ffa4' 
 , 
 '1c0dff' 
 ] 
 landcoverVisualization 
 = 
 { 
 'palette' 
 : 
 landcover_palette 
 , 
 'min' 
 : 
 0 
 , 
 'max' 
 : 
 16 
 , 
 'format' 
 : 
 'png' 
 } 
 Map 
 . 
 addLayer 
 ( 
 landsatComposite 
 . 
 classify 
 ( 
 savedClassifier 
 ), 
 landcoverVisualization 
 , 
 'Upsampled landcover, saved' 
 )