Here’s a comprehensive and technically detailed README tailored for uploading the implementation of the paper titled:

"Enhanced Landslide Detection by Remote Sensing Images Through Data Augmentation and Hybrid Deep Learning Model"

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📌 README.md for GitHub Repository

# Enhanced Landslide Detection using Hybrid CNN-BiLSTM Deep Learning Model

<p align="center">
  <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/d/d6/Landslide_in_Italy.jpg/640px-Landslide_in_Italy.jpg" width="500" />
</p>

## 🧠 Overview

This project provides a complete implementation of a **Hybrid Deep Learning Model** combining **Convolutional Neural Networks (CNN)** and **Bidirectional Long Short-Term Memory (BiLSTM)** for the **detection of landslide-prone areas** from remote sensing imagery. The approach is inspired by the research article:

> **Enhanced Landslide Detection by Remote Sensing Images Through Data Augmentation and Hybrid Deep Learning Model**  
> _Authors: Vikash Ranjan, Pradyut Kumar Biswal_  


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## 📂 Repository Structure

```bash
├── data/
│   ├── raw/                   # Original satellite images (Google Earth/Bhuvan)
│   ├── augmented/             # Augmented images (rotated, flipped, brightness adjusted)
│
├── models/
│   └── hybrid_cnn_bilstm.py   # Core model architecture
│
├── notebooks/
│   └── landslide_detection.ipynb  # Jupyter notebook for training and evaluation
│
├── utils/
│   └── preprocessing.py       # Data preprocessing and augmentation scripts
│
├── results/
│   └── metrics_report.json    # Precision, Recall, F1-Score, Accuracy
│
├── requirements.txt
├── README.md
└── train.py                   # Model training entry point

---

🖼️ Dataset Description

• Sources: Google Earth and ISRO’s Bhuvan platform
• Categories:
  • Landslide Images
  • Non-Landslide Images
• Image Size: All images are resized to 128x128x3 (RGB)
• Augmentation Techniques:
  • Rotations: 90°, 180°, 270°
  • Horizontal & Vertical Flipping
  • Brightness Adjustments

🧪 Augmentation helps increase generalization and model robustness.

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🧱 Model Architecture

🔹 CNN Block (Spatial Feature Extraction)

Layer	Parameters
Conv2D	3×3 filters, 32/64/128
Activation	ReLU
MaxPooling	2×2
Dropout	0.25

🔹 BiLSTM Block (Contextual/Sequential Modeling)

• BiLSTM with 128 hidden units
• Dropout: 0.5
• Learns both forward and backward dependencies of spatial features

🔹 Dense Layers (Classification)

• Dense (64), Activation: ReLU
• Dropout (0.5)
• Dense (1), Activation: Sigmoid

Final output is a binary class: 1 = Landslide, 0 = Non-Landslide

---

🔢 Technical Workflow

1. Data Preprocessing:

   • Resize images to 128×128
   • Normalize pixel values to [0, 1]
   • Augment training dataset to prevent overfitting

2. Model Training:

   • Optimizer: Adam
   • Loss: Binary Cross-Entropy
   • Batch Size: 32
   • Epochs: 50

3. Evaluation Metrics:

   • Accuracy
   • Precision
   • Recall
   • F1-Score

4. Result Visualization:

   • Confusion Matrix
   • ROC Curve
   • Loss vs Accuracy plots

---

📈 Results

Metric	Value
Accuracy	96.6%
Precision	94.6%
Recall	96.8%
F1-Score	95.6%

✅ Outperforms standalone CNN and LSTM models in both precision and recall.

---

🛠️ Installation

# Clone the repository
git clone https://github.com/yourusername/landslide-detection-hybrid-cnn-bilstm.git
cd landslide-detection-hybrid-cnn-bilstm

# Create virtual environment
python -m venv venv
source venv/bin/activate     # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

---

🚀 Run the Model

🔸 Train from Scratch

python train.py --data_dir ./data/augmented --epochs 50

🔸 Use Jupyter Notebook

jupyter notebook notebooks/landslide_detection.ipynb

---

📚 Dependencies

• Python 3.8+
• TensorFlow or PyTorch (your implementation choice)
• NumPy
• OpenCV
• Scikit-learn
• Matplotlib
• Pillow

See requirements.txt for the full list.

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🔍 Future Improvements

• Use of Vision Transformers (ViTs) for improved spatial context
• Integration of geospatial elevation maps
• Semi-supervised learning using pseudo-labeling
• Deployment via Flask/Django API

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💡 Citation

If you use this work or code, please cite:

@article{ranjan2024enhanced,
  title={Enhanced Landslide Detection by Remote Sensing Images Through Data Augmentation and Hybrid Deep Learning Model},
  author={Ranjan, Vikash and Biswal, Pradyut Kumar},
  
}

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🤝 Contributing

Pull requests and contributions are welcome! If you'd like to improve the model or add more datasets, feel free to fork and submit a PR.

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🛡️ License

This project is licensed under the MIT License. See the LICENSE file for details.

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📬 Contact

For questions or collaborations, please reach out via:

• 📧 Email: [[email protected]]
• 🔗 LinkedIn: [https://www.linkedin.com/in/vikash-ranjan-9273bb1a9/]

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⭐ Star History

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