#google #landsat #imageclassification
The Classifier package handles supervised classification by traditional ML algorithms running in Earth Engine. These classifiers include CART, RandomForest, NaiveBayes and SVM. The general workflow for classification is:
Collect training data. Assemble features which have a property that stores the known class label and properties storing numeric values for the predictors.
Instantiate a classifier. Set its parameters if necessary.
Train the classifier using the training data.
Classify an image or feature collection.
Estimate classification error with independent validation data.
The training data is a FeatureCollection with a property storing the class label and properties storing predictor variables. Class labels should be consecutive, integers starting from 0. If necessary, use remap() to convert class values to consecutive integers. The predictors should be numeric.Training and/or validation data can come from a variety of sources. To collect training data interactively in Earth Engine, you can use the geometry drawing tools (see the geometry tools section of the Code Editor page). Alternatively, you can import predefined training data from an Earth Engine table asset (see the Importing Table Data page for details). Get a classifier from one of the constructors in ee.Classifier. Train the classifier using classifier.train(). Classify an Image or FeatureCollection using classify().

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