Classification and Regression Trees (CART): Machine learning technique in Google Earth Engine.

In this tutorial learn how to:
1. Define sample points to train a classifier.
2. Classify the training points into different classes (Urban, Vegetation, Water, and Other Land)
3. Merge the four geometry layers into a single Feature Collection.
4. Sample the composite to generate training data. Note that the class label is stored in the ‘landcover’ property.
5. Train a CART classifier.
6. Do a Supervised Classification.
7. Split the data into 70% training, 30% testing. and classify the cloudfree composite.
8. Print the confusion matrix.
9. Determine the Land Cover Change between 2001 and 2019.

code link:
1. For Landsat 7 & 8 Collection 1 Tier 1 TOA Reflectance:
https://drive.google.com/file/d/13RFg...
2. For Landsat 7 Collection 1 Tier 1 Raw Scenes:
https://docs.google.com/document/d/1V...
3. For Title and Legends:
https://drive.google.com/file/d/162de...