Supervised Land Use Land Cover (LULC) Classification

https://code.earthengine.google.com/3... Learn how to perform Supervised Land Use Land Cover (LULC) Classification in Google Earth Engine (GEE) using the Random Forest Machine Learning algorithm with Sentinel-2 imagery. This complete tutorial covers the entire land cover mapping workflow, from Sentinel-2 preprocessing to feature engineering, classifier training, validation, accuracy assessment, and exporting the final classified map. 📌 In this tutorial, you'll learn: ✅ Load Sentinel-2 Surface Reflectance imagery ✅ Create a cloud-free median composite ✅ Calculate NDVI, NDBI, MNDWI, and Bare Soil Index (BSI) ✅ Prepare Ground Control Points (Training Samples) ✅ Merge multiple land cover classes ✅ Extract training samples using Sample Regions ✅ Train a Random Forest Classifier ✅ Perform Supervised Land Cover Classification ✅ Split training and validation datasets (60% / 40%) ✅ Generate a Confusion Matrix ✅ Calculate Overall Accuracy ✅ Calculate Producer's Accuracy ✅ Calculate User's (Consumer's) Accuracy ✅ Calculate Kappa Coefficient ✅ Calculate F1-Score ✅ Export the classified raster to Google Drive ✅ Export the ROI boundary ✅ Export the Accuracy Assessment Table (CSV) ✅ Export the classified image as an Earth Engine Asset 🛰 Dataset Used: • Sentinel-2 Surface Reflectance (COPERNICUS/S2_SR_HARMONIZED) 🌱 Spectral Indices: • NDVI (Normalized Difference Vegetation Index) • NDBI (Normalized Difference Built-up Index) • MNDWI (Modified Normalized Difference Water Index) • BSI (Bare Soil Index) 🤖 Machine Learning Algorithm: • Random Forest Classifier (Smile Random Forest) 🛰 Spatial Resolution: 10 meters 🎯 This tutorial is ideal for: • GIS Professionals • Remote Sensing Researchers • University Students • Environmental Scientists • Urban Planning • Agriculture Monitoring • Forest Mapping • Water Resource Management • Land Cover Change Detection • Machine Learning in Remote Sensing 📂 Outputs Generated: • Classified LULC Map • Confusion Matrix • Overall Accuracy • Producer's Accuracy • Consumer's Accuracy • Kappa Statistic • F1-Score • GeoTIFF Export • Earth Engine Asset Export • CSV Accuracy Report • ROI GeoJSON Export If you found this tutorial useful, please: 👍 Like the video 🔔 Subscribe for more Google Earth Engine, GIS, Remote Sensing, Python, ArcGIS Pro, and Machine Learning tutorials. 💬 Leave your questions in the comments. 📤 Share this tutorial with your friends and colleagues. #GoogleEarthEngine #GEE #RandomForest #SupervisedClassification #LandUseLandCover #LULC #LandCoverClassification #Sentinel2 #RemoteSensing #GIS #MachineLearning #NDVI #NDBI #MNDWI #BSI #AccuracyAssessment #ConfusionMatrix #EarthObservation #Geospatial #LearnGEE