// A Sentinel-2 surface reflectance image, reflectance bands selected,

//ROI สำหรับดึงภาพ Sentinel 2
//var countries = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017');
//var roi = countries.filter(ee.Filter.eq('country_na', 'Thailand'));

// A Sentinel-2 surface reflectance image, reflectance bands selected,
// serves as the source for training and prediction in this contrived example.
// Function to mask clouds using the Sentinel-2 QA band.
function maskS2clouds(image) {
  var qa = image.select('QA60')

  // Bits 10 and 11 are clouds and cirrus, respectively.
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;

  // Both flags should be set to zero, indicating clear conditions.
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(
             qa.bitwiseAnd(cirrusBitMask).eq(0))

  // Return the masked and scaled data, without the QA bands.
  return image.updateMask(mask)
              .select("B.*")
              .copyProperties(image, ["system:time_start"])
}
var img = ee.ImageCollection('COPERNICUS/S2_SR')
              .filterDate('2021-01-01', '2022-01-01')
              .filterBounds(roi)
              .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10))
              .map(maskS2clouds)
              .median();
// ESA WorldCover land cover map, used as label source in classifier training.
var lc = ee.Image('ESA/WorldCover/v100/2020');

// Remap the land cover class values to a 0-based sequential series.
var classValues = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 100];
var remapValues = ee.List.sequence(0, 10);
var label = 'lc';
lc = lc.remap(classValues, remapValues).rename(label).toByte();

// Add land cover as a band of the reflectance image and sample 100 pixels at
// 10 m scale from each land cover class within a region of interest.

var sample = img.addBands(lc).stratifiedSample({
  numPoints: 100,
  classBand: label,
  region: roi,
  scale: 10,
  tileScale: 4, 
  geometries: true
});

// Add a random value field to the sample and use it to approximately split 80%
// of the features into a training set and 20% into a validation set.
sample = sample.randomColumn();
var trainingSample = sample.filter('random <= 0.8');
var validationSample = sample.filter('random > 0.8');

// Train a 10-tree random forest classifier from the training sample.
var trainedClassifier = ee.Classifier.libsvm().train({
  features: trainingSample,
  classProperty: label,
  inputProperties: img.bandNames()
});

// Get information about the trained classifier.
print('Results of trained classifier', trainedClassifier.explain());

// Get a confusion matrix and overall accuracy for the training sample.
var trainAccuracy = trainedClassifier.confusionMatrix();
print('Training error matrix', trainAccuracy);
print('Training overall accuracy', trainAccuracy.accuracy());

// Get a confusion matrix and overall accuracy for the validation sample.
validationSample = validationSample.classify(trainedClassifier);
var validationAccuracy = validationSample.errorMatrix(label, 'classification');
print('Validation error matrix', validationAccuracy);
print('Validation accuracy', validationAccuracy.accuracy());

// Classify the reflectance image from the trained classifier.
var imgClassified = img.classify(trainedClassifier);

// Add the layers to the map.
var classVis = {
  min: 0,
  max: 10,
  palette: ['006400' ,'ffbb22', 'ffff4c', 'f096ff', 'fa0000', 'b4b4b4',
            'f0f0f0', '0064c8', '0096a0', '00cf75', 'fae6a0']
};
Map.centerObject(roi,13)
Map.addLayer(img.clip(roi), {bands: ['B4', 'B8', 'B3'], min: 100, max: 3500}, 'Sentinel-2');
Map.addLayer(lc.clip(roi), classVis, 'ESA WorldCover',false);
Map.addLayer(imgClassified.clip(roi), classVis, 'Classified');
Map.addLayer(roi, {color: 'white'}, 'ROI', false, 0.5);
Map.addLayer(trainingSample, {color: 'black'}, 'Training sample', false);
Map.addLayer(validationSample, {color: 'white'}, 'Validation sample', false);