A real-time aerial image analysis system that generates priority heatmaps for likely survivor zones in disaster scenarios. The system processes satellite and drone imagery to identify areas with high probability of human presence, considering building damage, accessibility, and temporal factors.
- Multi-Sensor Integration: Processes both satellite and drone imagery
- Real-time Mapping: Supports both batch and streaming image processing
- Priority Heatmap Generation: Outputs likelihood scores for survivor zones
- Explainable AI Layers:
- Building damage assessment
- Accessibility analysis
- Human-presence proxies
- Time since last coverage analysis
- Dynamic Resource Allocation: Supports path planning and resource optimization
Input Images → Preprocessing → Feature Extraction → Multi-Layer Analysis → Heatmap Generation → Output
↓ ↓ ↓ ↓ ↓
Satellite/Drone → Normalization → CNN/Transformer → Damage/Access/Human → Priority Map
- Install dependencies:
pip install -r requirements.txt- Run the system:
python main.py --input_path /path/to/images --output_path /path/to/output- For streaming mode:
python main.py --stream --input_source /path/to/stream --real_timeprober/
├── src/ # Source code
│ ├── preprocessing/ # Image preprocessing modules
│ ├── models/ # ML models for analysis
│ ├── analysis/ # Core analysis modules
│ ├── visualization/ # Heatmap and visualization
│ └── utils/ # Utility functions
├── data/ # Sample data and models
├── tests/ # Unit and integration tests
├── config/ # Configuration files
├── docs/ # Documentation
└── examples/ # Example scripts and notebooks
Please read our Contributing Guidelines before submitting pull requests.
This project is licensed under the MIT License - see the LICENSE file for details.
If you use this system in your research, please cite:
@software{disaster_survivor_detection,
title={Disaster Response Survivor Detection System},
author={Team Probe},
year={2025},
url={https://github.com/mashcodes10/teamprober}
}