GIS 

// Define the region of interest (roi) and add it to the map

Map.addLayer(roi);

var l8_composite = ee.ImageCollection('LANDSAT/LC08/C02/T1_RT_TOA')

                  .filterDate('2023-04-01', '2023-05-30')

                  .filterBounds(roi)

                  .filter(ee.Filter.lt('CLOUD_COVER', 5))

                  .median()

                  .clip(roi);

// add the layer to the map

var vis_params = {

  bands: ['B5', 'B4', 'B3'],

  min: 0,

  max: 3000,

  gamma: 1.2,

};

Map.addLayer(l8_composite, vis_params, 'L8 Composite');

// Merge the training points for all classes

var trainingPoint = Water.merge(Builtup).merge(Vegetation).merge(Agricultural).merge(Barren);

var trainingSample = trainingPoint.map(function(point){

  return point.buffer(30);

});

// Select bands for classification

var label = 'Class';

var bands = ['B1','B2','B3','B4','B5','B6','B7','B8','B9','B10','B11'];

// Here, you should be selecting bands from the image, not from roi

var input = l8_composite.select(bands);

// Sample regions to create training data

var trainImage = input.sampleRegions({

  collection: trainingSample,

  properties: [label],

  scale: 30

});

// Create training and test datasets

var trainingData = trainImage.randomColumn();

var trainSet = trainingData.filter(ee.Filter.lessThan('random', 0.8));

var testSet = trainingData.filter(ee.Filter.greaterThanOrEquals('random', 0.8));

// Classification with Random Forest

var classifier = ee.Classifier.smileRandomForest(64).train(trainSet, label, bands);

// Calculate classification accuracy/assessment

var confusionMatrix = ee.ConfusionMatrix(testSet.classify(classifier).errorMatrix({actual: 'Class', predicted: 'classification'}));

print('Confusion Matrix', confusionMatrix);

print('Overall Accuracy', confusionMatrix.accuracy());

// Kappa validation

var kappa = confusionMatrix.kappa();

print('Kappa', kappa);

// Apply classification to the image

var classified = input.classify(classifier);

// Show the classification result

Map.addLayer(classified, {

  palette: ['blue', 'red', 'yellow', 'green', 'orange'],

  min: 1,

  max: 5

}, 'Land Cover', true, 0.5);

// Export the classified image to Google Drive

Export.image.toDrive({

  image: classified,

  description: 'LULC',

  folder: 'GEE',

  scale: 30, 

  region: roi

});

Lecture 01 - English 

 Lecture 02- English