Update script.js

script.js

@@ -1,91 +1,60 @@
-// Machine-Learning-through-Google-Earth-Engine
-// Land Cover Classification using Sentinel-2 Imagery
-
-// This project uses Google Earth Engine to perform land cover classification on Sentinel-2 imagery for the year 2023.
-
-// Steps:
-// 1. Load Sentinel-2 image collection and preprocess the data.
-// 2. Merge and sample training data for different land cover classes.
-// 3. Train a Random Forest classifier on the sampled data.
-// 4. Classify the image and display the results.
-// 5. Evaluate the classifier performance using confusion matrices and accuracy.
-// 6. Export the training data for further analysis.
-
-// Loading and Preprocessing the Sentinel-2 Image Collection
-
-// Loading Sentinel-2 Imagery
+// Load Sentinel-2 image collection.
 var image = imageCollection.filterDate('2023-01-01', '2023-12-31').filterBounds(ddn).median();
-// The script loads Sentinel-2 images from the year 2023 over a specific region (ddn). 
-// The median() function is used to reduce the image collection to a single image, 
-// which represents the median pixel value across the year.
-
-// Selecting Bands and Adding Spectral Indices
+// Define a region of interest.
+// Select bands and create an image with spectral indices.
 var bands = ['B2', 'B3', 'B4', 'B8'];
-var image = image.select(bands).addBands(image.normalizedDifference(['B8', 'B4']).rename('NDVI'));
-// The code selects specific bands (B2, B3, B4, and B8) and adds the Normalized Difference Vegetation Index (NDVI) as an additional band. 
-// NDVI is computed using the near-infrared (B8) and red (B4) bands.
-
-// Display Parameters
-var displayparameters = { min: 1000, max: 4500, bands: ['B8', 'B4', 'B3'] };
-Map.addLayer(image, displayparameters, "Image");
-// The image is displayed on the map using the specified bands (B8, B4, B3) and display parameters.
-
-// Loading Training Data
+var image = image.select(bands).addBands(image.normalizedDifference(['B8',
+'B4']).rename('NDVI'));
+var displayparameters = {
+min: 1000,
+max: 4500,
+bands: ['B8', 'B4', 'B3'],
+};
+Map.addLayer(image,displayparameters,"Image");
+// Load training data (e.g., land cover classes).
 var label = "Class";
 var training = Water.merge(Forest).merge(Urban);
-// The script combines different land cover classes (Water, Forest, Urban) into a single training dataset and assigns them a label "Class".
-
-// Extracting Features for Training
-// Sampling Training Data
+// Extract features for training.
 var trainingimage = image.sampleRegions({
-  collection: training,
-  properties: [label],
-  scale: 10
-});
-// Features are extracted from the image for each training region. The sampling is done at a 10-meter scale.
-
-// Splitting Data into Training and Testing Sets
-// Randomly Splitting Data
+collection: training,
+properties: [label],
+scale: 10
+})
+// Divide input samples into Training & Testing
 var traingData = trainingimage.randomColumn();
-var trainSet = traingData.filter(ee.Filter.lessThan('random',0.8'));
-var testSet = traingData.filter(ee.Filter.greaterThanOrEquals('random',0.8'));
-// The data is split into training (80%) and testing (20%) sets using a random column.
-
-// Training the Random Forest Classifier
-// Training the Classifier
-var classifier = ee.Classifier.smileRandomForest({numberOfTrees:100, variablesPerSplit: 2, minLeafPopulation: 1, bagFraction: 0.5, seed: 0});
+var trainSet = traingData.filter(ee.Filter.lessThan('random',0.8));
+var testSet = traingData.filter(ee.Filter.greaterThanOrEquals('random',0.8));
+// Train a Random Forest classifier.
+//Define the classifier parameters:
+var classifier = ee.Classifier.smileRandomForest({numberOfTrees:100, variablesPerSplit: 2,
+minLeafPopulation: 1, bagFraction: 0.5,
+seed: 0});
+//Train the classifier on the training dataset
 var classifier = ee.Classifier.smileRandomForest(100).train(trainSet, label, bands);
-// The script trains a Random Forest classifier using the training set. The classifier uses 100 trees and the specified parameters
-
-// Classifying the Image
-// Applying the Classifier
+// Classify the image
+// Use the trained classifier to classify the entire dataset or the ROI
 var classified = image.classify(classifier);
-// The trained classifier is used to classify the entire image or region of interest.
-
-// Displaying the Classified Image
+// Display the results.
 Map.centerObject(ddn, 10);
 Map.addLayer(classified, {min: 1, max: 3, palette: ['green', 'blue', 'red']}, 'LandCover');
-// The classified image is displayed with a color palette representing different land cover classes.
-
-// Evaluating the Classifier
-// Training Accuracy
+//Get information about the trained classifier
+print('Results of trained classifier', classifier.explain());
+//Get a confusion matrix and overall accuracy for the training sample
 var trainAccuracy = classifier.confusionMatrix();
 print('Training error matrix', trainAccuracy);
 print('Training overall accuracy', trainAccuracy.accuracy());
-// The confusion matrix and accuracy for the training set are printed.
-
-// Validation Accuracy
+//Get a confusion matrix and overall accuracy for the validation sample.
 testSet = testSet.classify(classifier);
-var validationAccuracy = testSet.errorMatrix(label, 'classification');
+var validationAccuracy = testSet.errorMatrix(label,'classification');
 print('Validation error matrix', validationAccuracy);
 print('Validation accuracy', validationAccuracy.accuracy());
-// The script evaluates the classifier's performance on the test set and prints the validation confusion matrix and accuracy.
-
-// Exporting Data
-// Exporting to Google Earth Engine Asset
 Export.table.toAsset({
-  collection: training,
-  description: 'LCsample2023',
-  assetId: 'LCsample2023'
+collection: training,
+description: 'LCsample2023',
+assetId: 'LCsample2023'
+});
+Export.table.toDrive({
+collection: training,
+description: 'LCsample2023',
+fileFormat: 'SHP'
 });
-// The training data is also exported to Google Drive in SHP format.