//Access the boundary geometry of Raqqa
var extent_aoi = ee.FeatureCollection("FAO/GAUL/2015/level1")
                  .filter(ee.Filter.eq('ADM1_NAME', 'Raqqa')); //filter for entry that equals GAUL Level 1 feature named 'Raqqa'
print(extent_aoi, 'Extent AOI');
Map.addLayer(extent_aoi, {},'Extent AOI');
Map.centerObject(extent_aoi, 8);
 
 
//Access the Sentinel-2 Imagery of June 2020 with less than 10% cloud coverage
var s2a = ee.ImageCollection('COPERNICUS/S2_SR')
                  .filterBounds(extent_aoi)
                  .filterDate('2020-06-01', '2020-06-30')
                  .select('B1','B2','B3','B4','B5','B6','B7','B8','B8A','B9','B11','B12')
                  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10));
print(s2a, 'Image Collection Raqqa June 2020');
 
 
//Create a single Image by reducing by Median and clip it to the extent of the geometry
var s2a_median = s2a.median()
                    .clip(extent_aoi);
 
//Print your Image to your console tab to inspect it
print(s2a_median, 'Median reduced Image Raqqa June 2020');
 
//Add your Image as a map layer
var visParams = {'min': 400,'max': [4000,3000,3000],   'bands':'B8,B3,B2'};
Map.addLayer(s2a_median, visParams, 'S2 Raqqa June 2020 Median');


////////////////////////////////////////
//Start collecting Training Data here
///////////////////////////////////////


//Now that we defined our desired classes, we will need to merge them into one featureCollection for being able to use them as training data.
var classMerge = water.merge(vegetation).merge(urban).merge(baresoil);
print(classMerge, 'Merged Classes');


// Now, we can finally start collecting training data based on our defined classes.
// To do so, we will use .sampleRegions. Please check the documentation to find out more about how this works!
// Also, keep in mind that .sampleRegions only works for Images, not for ImageCollections!

var training = s2a_median.sampleRegions({
  collection: classMerge,
  properties: ['landcover'],
  scale: 30
});
print(training.limit(5000), 'Training Data');
 
 
 
//Classification using CART Classifier:
var cartclassifier = ee.Classifier.smileCart().train({
  features: training,
  classProperty: 'landcover'
});


//Run the classification
var cartclassified = s2a_median.classify(cartclassifier);

//Display classification
Map.addLayer(cartclassified,
{min: 0, max: 3, palette: ['blue', 'green', 'orange','yellow']},
'CART classification');
 
 
 
//Classification using RandomForest Classifier:
var rfclassifier = ee.Classifier.smileRandomForest(50).train({
  features: training,
  classProperty: 'landcover'
});


//Run the classification
var rfclassified = s2a_median.classify(rfclassifier);

//Display classification
Map.addLayer(rfclassified,
{min: 0, max: 3, palette: ['blue', 'green', 'orange','yellow']},
'RF classification');