https://github.com/krdevanshu06/forest-fire-detection-using-dl
A deep learning solution for detecting forest fires using sensor data and imagery. This repository includes a Jupyter Notebook that trains and evaluates models for early fire detection to improve disaster prevention.
https://github.com/krdevanshu06/forest-fire-detection-using-dl
cnn deep-learning forest-fire-detection tensorflow
Last synced: 2 months ago
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A deep learning solution for detecting forest fires using sensor data and imagery. This repository includes a Jupyter Notebook that trains and evaluates models for early fire detection to improve disaster prevention.
- Host: GitHub
- URL: https://github.com/krdevanshu06/forest-fire-detection-using-dl
- Owner: KrDevanshu06
- Created: 2025-04-03T11:00:35.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-03T11:16:55.000Z (over 1 year ago)
- Last Synced: 2025-04-09T21:12:31.266Z (about 1 year ago)
- Topics: cnn, deep-learning, forest-fire-detection, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 473 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Forest Fire Detection using Deep Learning
Forest Fire Detection using Deep Learning is a research and development project focused on creating an automated solution for early forest fire detection. Leveraging advanced deep learning techniques, this project aims to analyze sensor data and imagery to identify the onset of fires and provide timely alerts for effective response and mitigation.
## Table of Contents
- [Overview](#overview)
- [Motivation](#motivation)
- [Features](#features)
- [Technologies](#technologies)
- [Installation](#installation)
- [Usage](#usage)
- [Project Structure](#project-structure)
- [Contributing](#contributing)
- [Contact](#contact)
## Overview
This project demonstrates the use of deep learning methods to detect forest fires using an end-to-end workflow:
- **Data Preprocessing:** Techniques to prepare and augment sensor and image data.
- **Model Development:** Designing and training a deep learning model to recognize fire patterns.
- **Evaluation:** Assessing the model's performance with relevant metrics.
- **Deployment Considerations:** Outlining steps for real-time implementation and monitoring.
The primary implementation is provided in the Jupyter Notebook `Forest_Fire_Detection_using_DL.ipynb`.
## Motivation
Forest fires are a significant environmental and economic concern. Early detection is critical to minimize damage and enable rapid response. This project aims to harness deep learning's power to create an efficient, scalable, and accurate detection system that can be integrated with real-time monitoring systems.
## Features
- **Data Processing:** Prepares and normalizes both image and sensor datasets.
- **Deep Learning Model:** Implements a neural network tailored for fire detection.
- **Visualization:** Graphs and plots to visualize data distribution, model training progress, and performance metrics.
- **Evaluation Metrics:** Uses accuracy, precision, recall, and F1-score to assess model performance.
- **Extensibility:** Easy to integrate additional data sources or modify the model architecture.
## Technologies
- **Python:** Core programming language.
- **TensorFlow / Keras or PyTorch:** Deep learning frameworks (adjust according to your implementation).
- **Jupyter Notebook:** For interactive development and presentation.
- **NumPy, Pandas, Matplotlib:** For data manipulation and visualization.
- **Scikit-learn:** For additional machine learning utilities and evaluation.
## Installation
1. **Clone the Repository:**
```bash
git clone https://github.com/KrDevanshu06/Forest-Fire-Detection-using-DL.git
cd Forest_Fire_Detection_using_DL
```
2. **Set Up a Virtual Environment (Optional but Recommended):**
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
3. **Install Dependencies:**
```bash
pip install -r requirements.txt
```
## Usage
1. **Open the Notebook:**
- Launch Jupyter Notebook or Google Colab(if Using Browser):
```bash
jupyter notebook
```
- Open `Forest-Fire-Detection-using-DL.ipynb` to review and run the code.
2. **Run the Code Cells:**
- Follow the notebook sections to preprocess data, train the model, and evaluate performance.
3. **Customize and Extend:**
- Modify the notebook or scripts as needed for your specific dataset or requirements.
## Project Structure
```plaintext
Forest-Fire-Detection-using-DL
├── README.md
├── requirements.txt
└── Forest_Fire_Detection_using_DL.ipynb
```
## Contributing
Contributions are welcome! If you have suggestions or improvements, please follow these steps:
1. Fork the repository.
2. Create a new branch (`git checkout -b feature/your-feature`).
3. Commit your changes (`git commit -m 'Add some feature'`).
4. Push to the branch (`git push origin feature/your-feature`).
5. Open a Pull Request.
## Contact
For questions, feedback, or further information, please contact:
- **Name:** [Devanshu Kumar] [Shashwat Singh]
- **Email:** [KrDevanshu06@rediffmail.com] [singhshashwat521@gmail.com]
- **GitHub:** [KrDevanshu06](https://github.com/KrDevanshu06) [ShashwatSingh](https://github.com/shashwat-singh-01)