Performance comparison of machine learning algorithms for land cover classification

This dataset consists of shapefiles of training points names (SamplePoints.zip) which were collected on the Google Earth Engine platform for our land cover analysis.

The code.zip contains the codes and the direct Google Earth Engine code showing how the land cover analysis with six (6) machine learning algorithms (Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Gradient Tree Boost (GTB), Classification and Regression Tree (CART), and Naive Bayes) was implemented. Each link to a code has been annotated with the correct season (either fall, spring, summer, and winter). Additionally, these links have been added as related links to this dataset strictly in the order Fall > Spring > Summer > Winter.