https://github.com/agora-lab-ai/forestnet
A Deep Learning Framework for Quantifying Collective Forest Intelligence Through Multi-Variable Temporal-Spatial Analysis
https://github.com/agora-lab-ai/forestnet
ai amazon amazonforests bioinformatics biology biologyai bioml collective-behavior collective-intelligence forest-ai forests foundation-models greenery greenery-ai ml swarms
Last synced: 4 months ago
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A Deep Learning Framework for Quantifying Collective Forest Intelligence Through Multi-Variable Temporal-Spatial Analysis
- Host: GitHub
- URL: https://github.com/agora-lab-ai/forestnet
- Owner: Agora-Lab-AI
- License: mit
- Created: 2024-11-08T04:13:29.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-20T11:31:31.000Z (over 1 year ago)
- Last Synced: 2025-07-22T17:52:17.262Z (11 months ago)
- Topics: ai, amazon, amazonforests, bioinformatics, biology, biologyai, bioml, collective-behavior, collective-intelligence, forest-ai, forests, foundation-models, greenery, greenery-ai, ml, swarms
- Language: Python
- Homepage: https://discord.com/servers/agora-999382051935506503
- Size: 33.2 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
# ForestNet Deep Learning Framework for Forest Intelligence Analysis
[](https://discord.gg/agora-999382051935506503) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](https://x.com/kyegomezb)
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
[](https://pytorch.org/)
## Overview
ForestNet 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.
### Key Features
- Multi-scale temporal-spatial analysis of forest ecosystems
- Integration of multiple environmental variables
- Advanced LSTM-based predictive modeling
- Quantifiable intelligence metrics
- High-resolution data processing (50x50 grid)
- 5-year temporal analysis window
## Architecture
```mermaid
graph TD
A[Data Collection] -->|MODIS Satellite Data| B[Data Processing]
B --> C[Feature Engineering]
C --> D[Neural Network]
subgraph "Data Sources"
A1[NDVI] --> A
A2[Temperature] --> A
A3[Precipitation] --> A
A4[Soil Moisture] --> A
A5[Solar Radiation] --> A
end
subgraph "Processing Pipeline"
B1[Spatial Smoothing] --> B
B2[Temporal Alignment] --> B
B3[Quality Control] --> B
end
subgraph "Neural Architecture"
D1[LSTM Layers] --> D
D2[Attention Mechanism] --> D
D3[Dense Layers] --> D
end
D --> E[Intelligence Metrics]
subgraph "Output Metrics"
E1[Prediction Accuracy]
E2[Synchronization Score]
E3[Adaptive Capacity]
end
```
## Data Structure
```mermaid
sequenceDiagram
participant S as Satellite Data
participant P as Preprocessor
participant M as Model
participant E as Evaluator
S->>P: Raw MODIS Data
P->>P: Spatial Smoothing
P->>P: Variable Integration
P->>M: Processed Tensors
M->>M: LSTM Processing
M->>E: Predictions
E->>E: Calculate Metrics
```
## Installation
```bash
# Clone the repository
git clone https://github.com/Agora-Lab-AI/ForestNet.git
cd ForestNet
# Install dependencies
pip install -r requirements.txt
```
## Usage
```python
# Train the model
python3 main.py
```
## Dataset Description
SylvaNet utilizes multiple environmental variables collected over a 5-year period:
| Variable | Resolution | Frequency | Source |
|----------|------------|-----------|---------|
| NDVI | 50x50 grid | Daily | MODIS |
| Temperature | 50x50 grid | Daily | MODIS |
| Precipitation | 50x50 grid | Daily | MODIS |
| Soil Moisture | 50x50 grid | Daily | MODIS |
| Solar Radiation | 50x50 grid | Daily | MODIS |
## Model Performance
Intelligence metrics are calculated across three dimensions:
1. **Prediction Accuracy** (0-1)
- Measures the model's ability to predict forest behavior
- Typical range: 0.5-0.8
2. **Synchronization Score** (0-1)
- Quantifies coordinated responses across forest regions
- Typical range: 0.3-0.6
3. **Adaptive Capacity** (0-1)
- Evaluates forest learning and adaptation
- Typical range: 0.4-0.7
## Todo List
- [ ] Implement multi-GPU training support
- [ ] Add support for additional satellite data sources
- [ ] Integrate ground-based sensor data
- [ ] Develop visualization dashboard
- [ ] Add automated hyperparameter optimization
- [ ] Implement ensemble learning approaches
- [ ] Add support for real-time data processing
- [ ] Create API for external data integration
- [ ] Develop transfer learning capabilities
- [ ] Add detailed documentation and tutorials
## Research Team
- Principal Investigators: Kye Gomez
- Institution: Agora
- Lab: Agora Lab AI
- Contact: kye@swarms.world
## Citation
If you use ForestNet in your research, please cite:
```bibtex
@article{ForestNet2024,
title={ForestNet: A Deep Learning Framework for Quantifying Collective Forest Intelligence},
author={Kye Gomez et al.},
year={2024},
volume={},
pages={},
publisher={}
}
```
## Contributing
We welcome contributions! Please see our [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
## License
This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.
## Acknowledgments
- MODIS Science Team
- PyTorch Development Team
- agoralab.ai
## 📬 Contact
Questions? Reach out:
- Twitter: [@kyegomez](https://twitter.com/kyegomez)
- Email: kye@swarms.world
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