ForestNet Deep Learning Framework for Forest Intelligence Analysis

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

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

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Data Structure

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

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Installation

# Clone the repository
git clone https://github.com/Agora-Lab-AI/ForestNet.git
cd ForestNet

# Install dependencies
pip install -r requirements.txt

Usage

# 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:

@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 for guidelines.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Acknowledgments

• MODIS Science Team
• PyTorch Development Team
• agoralab.ai

📬 Contact

Questions? Reach out:

• Twitter: @kyegomez
• Email: kye@swarms.world

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