🛰️ Satellite Land Cover Classification

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📋 Project Overview

This project focuses on Automated Land Cover Classification (LCC) using Deep Learning. By implementing a ResNet-50 Convolutional Neural Network, the model successfully classifies high-resolution satellite imagery into 10 distinct environmental categories. This mirrors industry-standard workflows used by organizations such as NRSC/ISRO for geospatial analysis.

🏗️ Technical Architecture

• Backbone: ResNet-50 (Transfer Learning from ImageNet)
• Data Pipeline: Customized torchvision transforms including normalization and random flips.
• Classification Head: Fully connected layers with Dropout (0.3) to prevent overfitting.
• Optimization: Adam Optimizer with Negative Log-Likelihood Loss (NLLLoss).

📊 Dataset Details

The model was trained on the EuroSAT (RGB) dataset, derived from Sentinel-2 satellite imagery. Classes include: Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial, Pasture, Permanent Crop, Residential, River, Sea Lake.

📈 Key Performance Results

• Accuracy: ~96% on Validation Set.
• Efficiency: Leveraged GPU acceleration (T4) in Colab for fast convergence.
• Evaluation: Performance verified via Confusion Matrix and F1-score analysis.

🛠️ How to Run

1. Install the required libraries:

   pip install torch torchvision matplotlib seaborn scikit-learn tqdm