🌿 Nature Image Classification - CNN

Nature Image Classification is a deep learning–based computer vision project designed to automatically classify images of natural scenes into predefined categories such as forest, mountain, sea, desert, glacier, and buildings.

The project demonstrates how Convolutional Neural Networks (CNNs) can be applied to real-world image classification problems using Python and TensorFlow/Keras. It covers the complete pipeline—from data preprocessing and model training to evaluation and prediction.

🎯 Objectives

• Build an accurate image classification model for natural scene images
• Apply deep learning techniques for feature extraction
• Improve generalization using data augmentation
• Evaluate model performance using standard metrics
• Provide an easy-to-use prediction pipeline

🗂️ Dataset Description

• Type: Labeled image dataset of natural scenes

• Categories:

  • Forest
  • Mountain
  • Sea
  • Desert
  • Glacier
  • Buildings

• Data Split:

  • Training set
  • Validation set
  • Test set

⚠️ Dataset source depends on user implementation (e.g., local dataset, academic dataset, or public repository). If you are using a public dataset, please cite the original source accordingly.

🧠 Model Architecture

The project uses a Convolutional Neural Network (CNN) with the following components:

• Convolutional layers for feature extraction
• ReLU activation functions
• MaxPooling layers for spatial reduction
• Fully connected (Dense) layers
• Softmax output layer for multi-class classification

Optional enhancements:

• Dropout for regularization
• Batch normalization
• Transfer learning (e.g., VGG16, ResNet, MobileNet)

🛠️ Technologies Used

• Programming Language: Python

• Libraries & Frameworks:

  • TensorFlow / Keras
  • NumPy
  • Matplotlib
  • OpenCV (optional)
  • Scikit-learn

📊 Model Evaluation

To evaluate the trained model on the test dataset:

python src/evaluate.py

Evaluation metrics include:

• Accuracy
• Loss
• Confusion Matrix
• Classification Report

🔒 Limitations

• Performance may drop on low-quality or unseen image types
• Sensitive to lighting, resolution, and viewpoint variations
• Requires sufficient labeled data for best accuracy

🌱 Future Improvements

• Apply advanced transfer learning models
• Increase dataset size and diversity
• Add real-time image classification support
• Deploy as a web or mobile application

👤 Author

HOSEN ARAFAT

Software Engineer, China

GitHub: https://github.com/arafathosense

Researcher: Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Image Processing