{"id":19492659,"url":"https://github.com/agora-lab-ai/forestnet","last_synced_at":"2026-02-06T23:32:19.310Z","repository":{"id":261728614,"uuid":"885159537","full_name":"Agora-Lab-AI/ForestNet","owner":"Agora-Lab-AI","description":"A Deep Learning Framework for Quantifying Collective Forest Intelligence Through Multi-Variable Temporal-Spatial 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Framework for Forest Intelligence Analysis\n\n[![Join our Discord](https://img.shields.io/badge/Discord-Join%20our%20server-5865F2?style=for-the-badge\u0026logo=discord\u0026logoColor=white)](https://discord.gg/agora-999382051935506503) [![Subscribe on YouTube](https://img.shields.io/badge/YouTube-Subscribe-red?style=for-the-badge\u0026logo=youtube\u0026logoColor=white)](https://www.youtube.com/@kyegomez3242) [![Connect on LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue?style=for-the-badge\u0026logo=linkedin\u0026logoColor=white)](https://www.linkedin.com/in/kye-g-38759a207/) [![Follow on X.com](https://img.shields.io/badge/X.com-Follow-1DA1F2?style=for-the-badge\u0026logo=x\u0026logoColor=white)](https://x.com/kyegomezb)\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)\n[![PyTorch](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?style=flat\u0026logo=PyTorch\u0026logoColor=white)](https://pytorch.org/)\n\n## Overview\n\nForestNet is a novel deep learning framework designed to analyze and quantify collective forest intelligence through multi-variable temporal-spatial analysis. This research explores the hypothesis that forests exhibit emergent intelligent behaviors through their collective responses to environmental changes and stressors.\n\n### Key Features\n- Multi-scale temporal-spatial analysis of forest ecosystems\n- Integration of multiple environmental variables\n- Advanced LSTM-based predictive modeling\n- Quantifiable intelligence metrics\n- High-resolution data processing (50x50 grid)\n- 5-year temporal analysis window\n\n## Architecture\n\n```mermaid\ngraph TD\n    A[Data Collection] --\u003e|MODIS Satellite Data| B[Data Processing]\n    B --\u003e C[Feature Engineering]\n    C --\u003e D[Neural Network]\n    \n    subgraph \"Data Sources\"\n    A1[NDVI] --\u003e A\n    A2[Temperature] --\u003e A\n    A3[Precipitation] --\u003e A\n    A4[Soil Moisture] --\u003e A\n    A5[Solar Radiation] --\u003e A\n    end\n    \n    subgraph \"Processing Pipeline\"\n    B1[Spatial Smoothing] --\u003e B\n    B2[Temporal Alignment] --\u003e B\n    B3[Quality Control] --\u003e B\n    end\n    \n    subgraph \"Neural Architecture\"\n    D1[LSTM Layers] --\u003e D\n    D2[Attention Mechanism] --\u003e D\n    D3[Dense Layers] --\u003e D\n    end\n    \n    D --\u003e E[Intelligence Metrics]\n    \n    subgraph \"Output Metrics\"\n    E1[Prediction Accuracy]\n    E2[Synchronization Score]\n    E3[Adaptive Capacity]\n    end\n```\n\n## Data Structure\n\n```mermaid\nsequenceDiagram\n    participant S as Satellite Data\n    participant P as Preprocessor\n    participant M as Model\n    participant E as Evaluator\n    \n    S-\u003e\u003eP: Raw MODIS Data\n    P-\u003e\u003eP: Spatial Smoothing\n    P-\u003e\u003eP: Variable Integration\n    P-\u003e\u003eM: Processed Tensors\n    M-\u003e\u003eM: LSTM Processing\n    M-\u003e\u003eE: Predictions\n    E-\u003e\u003eE: Calculate Metrics\n\n```\n\n## Installation\n\n```bash\n# Clone the repository\ngit clone https://github.com/Agora-Lab-AI/ForestNet.git\ncd ForestNet\n\n# Install dependencies\npip install -r requirements.txt\n```\n\n## Usage\n\n```python\n# Train the model\npython3 main.py\n```\n\n## Dataset Description\n\nSylvaNet utilizes multiple environmental variables collected over a 5-year period:\n\n| Variable | Resolution | Frequency | Source |\n|----------|------------|-----------|---------|\n| NDVI | 50x50 grid | Daily | MODIS |\n| Temperature | 50x50 grid | Daily | MODIS |\n| Precipitation | 50x50 grid | Daily | MODIS |\n| Soil Moisture | 50x50 grid | Daily | MODIS |\n| Solar Radiation | 50x50 grid | Daily | MODIS |\n\n## Model Performance\n\nIntelligence metrics are calculated across three dimensions:\n\n1. **Prediction Accuracy** (0-1)\n   - Measures the model's ability to predict forest behavior\n   - Typical range: 0.5-0.8\n\n2. **Synchronization Score** (0-1)\n   - Quantifies coordinated responses across forest regions\n   - Typical range: 0.3-0.6\n\n3. **Adaptive Capacity** (0-1)\n   - Evaluates forest learning and adaptation\n   - Typical range: 0.4-0.7\n\n## Todo List\n\n- [ ] Implement multi-GPU training support\n- [ ] Add support for additional satellite data sources\n- [ ] Integrate ground-based sensor data\n- [ ] Develop visualization dashboard\n- [ ] Add automated hyperparameter optimization\n- [ ] Implement ensemble learning approaches\n- [ ] Add support for real-time data processing\n- [ ] Create API for external data integration\n- [ ] Develop transfer learning capabilities\n- [ ] Add detailed documentation and tutorials\n\n## Research Team\n\n- Principal Investigators: Kye Gomez\n- Institution: Agora\n- Lab: Agora Lab AI\n- Contact: kye@swarms.world\n\n## Citation\n\nIf you use ForestNet in your research, please cite:\n\n```bibtex\n@article{ForestNet2024,\n  title={ForestNet: A Deep Learning Framework for Quantifying Collective Forest Intelligence},\n  author={Kye Gomez et al.},\n  year={2024},\n  volume={},\n  pages={},\n  publisher={}\n}\n```\n\n## Contributing\n\nWe welcome contributions! Please see our [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.\n\n## Acknowledgments\n\n- MODIS Science Team\n- PyTorch Development Team\n- agoralab.ai\n\n\n## 📬 Contact\n\nQuestions? Reach out:\n- Twitter: [@kyegomez](https://twitter.com/kyegomez)\n- Email: kye@swarms.world\n\n---\n\n## Want Real-Time Assistance?\n\n[Book a call with here for real-time assistance:](https://cal.com/swarms/swarms-onboarding-session)\n\n---\n\n⭐ Star us on GitHub if this project helped you!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fagora-lab-ai%2Fforestnet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fagora-lab-ai%2Fforestnet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fagora-lab-ai%2Fforestnet/lists"}