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🚀 Data Augmentation & Feature Engineering in Remote Sensing Classification Remote sensing MSc Course Lecture🌍 In this session, we tackle one of the core limitations in remote sensing classification: scarce labeled data and class imbalance. We build a structured framework combining augmentation strategies and feature engineering to improve model robustness in real-world conditions. Designed for: small datasets, noisy labels, and heterogeneous landscapes. 🔍 What’s Inside 🟡 Bird View (Block 1) Understanding the Landsat 5 & 7, MODIS, DEM, and IKONOS data📉 🟡 Visualization and Re-Projection (Block 2) See which sites do we have, along with the referenced data, and set a common CRS to all of them. 🟡 Data-Augmentations (Block 3) Geometric & photometric transformations (rotation, scaling, noise injection) 🔄 SMOTE, Borderline-SMOTE, ADASYN MixUp, CutMix, Manifold Mixup 🧪 GANs & diffusion models for synthetic data generation 🤖✨ 🟡 Performance Benchmarking (Block 4) End-to-end evaluation for Methods🐍 🟡 Visualize Predictions (Block 5) Comparative the predicted LULC along all sites 📊 📊 Outputs Classification accuracy & F1-score comparisons Confusion matrices Feature importance rankings Model robustness analysis under data scarcity ⚙️ Key Techniques Multi-source feature construction Data augmentation under limited samples Feature selection (filter + embedded methods) Generalization improvement in ML/DL models 💡 Tools & Libraries: scikit-learn, numpy, pandas, matplotlib, scipy #RemoteSensing #MachineLearning #DataAugmentation #FeatureEngineering #GIS #Photogrammetry #DeepLearning #Python If you're into scientific ML, remote sensing workflows, and real-world data challenges, hit 👍 and subscribe for more 🚀