An advanced AI-powered system for real-time wildfire detection using satellite imagery and environmental data analysis. The system combines computer vision technology with weather data to provide comprehensive wildfire risk assessment and early detection capabilities.
- Real-time satellite image analysis for wildfire detection
- Weather condition monitoring and risk assessment
- 95% accuracy in wildfire detection
- Comprehensive risk rating system
- Live coordinate-based weather data integration
- Convolutional Neural Network (CNN) for image classification
- Trained on 2000+ images
- 95% detection accuracy
- Multi-dataset training approach
-
VisDrone2019-DET Dataset
- Aerial imagery dataset
- High-resolution drone captures
- Dataset Link
-
Wildfire Prediction Dataset
- Specialized wildfire imagery
- Ground truth annotations
- Dataset Link
- Real-time weather API integration
- Location-based weather data retrieval
- Environmental condition assessment
- Risk factor calculation
- Model Accuracy: 95%
- Training Dataset Size: 2000+ images
- Real-time Processing Capability
- Weather Data Update Frequency: Real-time
- Python (Machine Learning & Backend)
- TensorFlow/PyTorch (Deep Learning)
- React.js (Frontend) + MapBox API
- Weather API Integration
- Satellite Image Processing Libraries
This project is licensed under the MIT License - see the LICENSE.md file for details.
- Luan Nguyen - Full-stack Developer - https://www.linkedin.com/in/luanthiennguyen/
- Nga Vu - ML Engineer - https://www.linkedin.com/in/nga-vu-269626226/
- Smit Devrukhkar - DevOps Developer - https://www.linkedin.com/in/smitsd/
- Andy Le - Full-stack Developer - https://www.linkedin.com/in/4ndyle/
- VisDrone Team for their comprehensive drone imagery dataset
- Kaggle community for the Wildfire Prediction Dataset
- Weather API providers for real-time environmental data