🛰️ EuroSAT Satellite Image Classification using CNN

📖 Project Overview

This project implements a Deep Learning model for classifying satellite images from the EuroSAT dataset. The model uses a Convolutional Neural Network (CNN) to recognize and classify land use patterns from satellite imagery captured by the Sentinel-2 satellite.

👨‍💻 Author

Masoud Ghasemi

GitHub: sorna-fast
Email: [email protected]
linkedin: masoud-ghasemi
Telegram: @Masoud_Ghasemi_sorna_fast

🗂️ Project Structure

.
├── 📁 datasets/                 # Original dataset directory
│   └── EuroSAT_extracted/
│       └── 2750/               # Main dataset folder
├── 📁 model/                   # Trained models
│   └── best_model_epoch.keras  # Best trained model
├── 📁 notebooks/               # Jupyter notebooks
│   └── satellite-and-classifier.ipynb # EuroSAT CNN training notebook
├── 📁 visualizations/          # Generated plots and charts
│   ├── class_distribution.png  # Dataset class distribution
│   ├── confusion_matrix.png    # Model confusion matrix
│   ├── dataset_samples.png     # Sample images from dataset
│   └── training_history.png    # Training progress plots
├── 📄 requirements.txt         # Python dependencies
├── 📄 README.md               # Project documentation
└── 📄 .gitignore              # Git ignore file

📊 Dataset Information

EuroSAT Dataset

Total Images: 27,000
Image Size: 64x64 pixels
Channels: RGB (3 channels)
Classes: 10 land use types

Land Use Classes

AnnualCrop
Forest
HerbaceousVegetation
Highway
Industrial
Pasture
PermanentCrop
Residential
River
SeaLake

🏗️ Model Architecture

Model Summary

Layer (type)	Output Shape	Param #
data_augmentation (Sequential)	(None, 64, 64, 3)	0
conv2d_12 (Conv2D)	(None, 64, 64, 64)	1,792
batch_normalization_12 (BatchNormalization)	(None, 64, 64, 64)	256
conv2d_13 (Conv2D)	(None, 64, 64, 64)	36,928
batch_normalization_13 (BatchNormalization)	(None, 64, 64, 64)	256
max_pooling2d_6 (MaxPooling2D)	(None, 32, 32, 64)	0
conv2d_14 (Conv2D)	(None, 32, 32, 256)	147,712
batch_normalization_14 (BatchNormalization)	(None, 32, 32, 256)	1,024
max_pooling2d_7 (MaxPooling2D)	(None, 16, 16, 256)	0
conv2d_15 (Conv2D)	(None, 16, 16, 384)	885,120
batch_normalization_15 (BatchNormalization)	(None, 16, 16, 384)	1,536
conv2d_16 (Conv2D)	(None, 16, 16, 384)	1,327,488
batch_normalization_16 (BatchNormalization)	(None, 16, 16, 384)	1,536
conv2d_17 (Conv2D)	(None, 16, 16, 256)	884,992
batch_normalization_17 (BatchNormalization)	(None, 16, 16, 256)	1,024
max_pooling2d_8 (MaxPooling2D)	(None, 8, 8, 256)	0
global_average_pooling2d_2 (GlobalAveragePooling2D)	(None, 256)	0
dense_6 (Dense)	(None, 256)	65,792
dropout_4 (Dropout)	(None, 256)	0
dense_7 (Dense)	(None, 256)	65,792
dropout_5 (Dropout)	(None, 256)	0
dense_8 (Dense)	(None, 10)	2,570

Total Parameters: 3,423,618

📈 Training Results & Performance

Training Progress

The model was trained for 33 epochs with early stopping. Key training milestones:

Best Model: Saved at Epoch 28 with 96.44% validation accuracy
Final Training Accuracy: 97.06% (Epoch 28)
Training Stopped: Epoch 33 due to early stopping
Learning Rate Adjustments: Automatically reduced 4 times for optimal convergence

Training History

Model Evaluation Results

Overall Performance

Overall Accuracy: 96%
Macro Average F1-Score: 0.96
Weighted Average F1-Score: 0.96

Class-wise Accuracy

Class	Accuracy
AnnualCrop	93.3%
Forest	99.8% (Best)
HerbaceousVegetation	96.0%
Highway	99.0%
Industrial	98.0%
Pasture	94.1%
PermanentCrop	93.7%
Residential	98.4%
River	96.7%
SeaLake	94.9%

Key Findings

Best Performing Class: Forest (99.8% accuracy)
Most Challenging Class: AnnualCrop (93.3% accuracy)
Most Common Misclassification: AnnualCrop → PermanentCrop (27 samples)

Confusion Matrix

Detailed Classification Report

==================================================
Full Classification Report:
==================================================
                      precision    recall  f1-score   support

          AnnualCrop       0.96      0.93      0.95       597
              Forest       0.94      1.00      0.97       600
HerbaceousVegetation       0.94      0.96      0.95       557
             Highway       0.98      0.99      0.98       493
          Industrial       0.97      0.98      0.98       512
             Pasture       0.98      0.94      0.96       374
       PermanentCrop       0.92      0.94      0.93       491
         Residential       0.98      0.98      0.98       607
               River       0.98      0.97      0.97       547
             SeaLake       1.00      0.95      0.97       622

            accuracy                           0.96      5400
           macro avg       0.96      0.96      0.96      5400
        weighted avg       0.97      0.96      0.96      5400

⚙️ Installation & Setup

Prerequisites

Python 3.8+
TensorFlow 2.x
Jupyter Notebook

Installation Steps

Create virtual environment

python -m venv myvenv
source myvenv/bin/activate  # On Windows: myvenv\Scripts\activate

Install dependencies

pip install -r requirements.txt

🔧 Hardware Configuration

Important Hardware Configuration Note

⚠️ Note: If you are not using Google Colab, GPU processing settings may differ:

In Google Colab: Uses Tesla T4 or similar GPU by default
In local environment: Requires manual installation of CUDA and cuDNN drivers
Memory settings: You may need to reduce batch size on systems with less GPU memory

# Check GPU support
import tensorflow as tf
print("Number of available GPUs: ", len(tf.config.list_physical_devices('GPU')))

Optimal Settings for Different Systems

GPU with 8GB+ memory: BATCH_SIZE = 32
GPU with 4-8GB memory: BATCH_SIZE = 16
CPU only: BATCH_SIZE = 8

🚀 Usage

Running the Project

Open Jupyter Notebook

jupyter notebook

Choose your preferred notebook:
- satellite-and-classifier-en.ipynb - English version
Execute cells sequentially to:
- Download and preprocess data
- Train the CNN model
- Evaluate model performance
- Generate visualizations

Key Configuration

# Model parameters
IMG_SIZE = (64, 64)        # Input image size
BATCH_SIZE = 32           # Training batch size
EPOCHS = 40               # Maximum training epochs

# Data augmentation
data_augmentation = tf.keras.Sequential([
    RandomFlip("horizontal_and_vertical"),
    RandomRotation(0.25),
    RandomTranslation(0.2, 0.2),
    RandomZoom(0.2)
])

💡 Key Features

Data Handling

✅ Automatic dataset download and extraction
✅ Image preprocessing and normalization
✅ Efficient data pipeline with caching
✅ Balanced dataset handling

Model Development

✅ Custom CNN architecture
✅ Comprehensive data augmentation
✅ Batch normalization for stable training
✅ Dropout for regularization

Training Management

✅ Automatic checkpointing
✅ Early stopping
✅ Learning rate scheduling
✅ Progress monitoring

Evaluation & Visualization

✅ Confusion matrix analysis
✅ Classification reports
✅ Training history plots
✅ Sample image visualization

🎓 Applications

This model can be used for:

Land use mapping and monitoring
Environmental research
Urban planning
Agricultural monitoring
Disaster management
Climate change studies

📜 License

This project is for educational purposes. The EuroSAT dataset is provided for research use.

🙏 Acknowledgments

EuroSAT Dataset: Helber et al. for the satellite imagery dataset
TensorFlow Team: For the deep learning framework
Sentinel-2 Mission: For providing satellite imagery

🌿 Using AI to understand our planet better

Achieved 96.4% Accuracy in Satellite Image Classification

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
datasets/EuroSAT_extracted/2750		datasets/EuroSAT_extracted/2750
model		model
notebooks		notebooks
visualizations		visualizations
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
requirements.txt		requirements.txt

License

sorna-fast/eurosat-cnn-classifier

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