Amazon Archaeological Discovery Platform

Overview

The Amazon Archaeological Discovery Platform is designed to predict and analyze archaeological sites within the Amazon Basin, integrating advanced machine learning models, satellite data, indigenous group information, and professional computer vision techniques. The platform uses real-world data to predict the locations of potential archaeological sites, providing a deeper understanding of ancient civilizations and their environmental interactions.

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Table of Contents

1. Features
2. Project Structure
3. Requirements
4. Installation Guide
5. Running Your First Example
6. Usage
7. Methodology
8. Contributing
9. License
10. Google Earth Engine Authentication

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Features

1. Archaeological Site Prediction

• Predict archaeological site locations using machine learning algorithms based on historical, environmental, and indigenous data.

2. Satellite Data Integration

• Incorporate satellite imagery from NASA and ESA to enhance the prediction of archaeological sites.

3. AI-Powered Analysis

• Use OpenAI GPT-4 for generating detailed reports and analyses of archaeological findings.

4. Geospatial Mapping

• Visualize archaeological sites and their proximity to geographical features (rivers, elevation) using Folium and GeoPandas.

5. Data Logging and Error Handling

• Advanced logging system tracks operations, API requests, and analysis outcomes to ensure smooth processing.

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Project Structure

amazon-archaeological-platform/
├── Research.py  # Research dashboard
├── Discovery.py   # Discovery dashboard
├── requirements.txt                            # Python dependencies
├── README.md                                   # Project overview and instructions
└── LICENSE                                     # MIT License

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Requirements

• Python: Version 3.11
• Streamlit: For building the web interface
• Scikit-learn: For machine learning algorithms (RandomForest, SVM, etc.)
• GeoPandas: For geospatial data processing (optional)
• Plotly: For visualizing geospatial data and analyses

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Installation Guide

1. Clone the repository:

git clone https://github.com/OpenAI-prodigal.git
cd OpenAI-prodigal

2. Create and activate a Python virtual environment:

python3.12 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install the required dependencies:

pip install -r requirements.txt

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Running Your First Example

1. Run the Streamlit app:

streamlit run Research.py

streamlit run Discovery.py

2. The system will ask for API keys and credentials.

3. The platform will analyze the data and provide archaeological site predictions and insights.

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Methodology

1. Data Collection

   • The platform integrates data from:

     • Verified archaeological site records
     • Environmental and geospatial data (satellite imagery, soil types, river systems)
     • Indigenous territories and traditional earthworks

2. Machine Learning Models

   • Feature Engineering: Includes data like latitude, longitude, elevation, rainfall, and structure count.
   • Model Training: Models are trained on historical site data to predict site locations.

3. Geospatial Analysis

   • Uses GeoPandas and Folium to visualize site locations in relation to geographical features.

4. AI-Powered Insights

   • OpenAI GPT-4 provides in-depth analysis of predictions and generates detailed archaeological reports.

5. Site Clustering

   • DBSCAN clustering groups nearby archaeological sites, identifying regions of historical significance.

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Google Earth Engine Authentication

To use Earth Engine (GEE) in this platform, you must authenticate your account and set up the project ID for your platform's integration with GEE.

Steps to Authenticate GEE:

1. Create a Google Cloud Project and Enable Earth Engine:

   • Navigate to the Google Cloud Console.
   • Create a new project, or select an existing project.
   • Search for Google Earth Engine API in the APIs & Services section and enable it.
   • Under the IAM & Admin section, ensure that you have the necessary permissions, especially Viewer or Editor.

2. Get Your Project ID:

   • Once your project is set up, navigate to the Dashboard of the Google Cloud Console.
   • You'll find your Project ID in the top right corner under the Project Info section. This is the ID you'll use for authentication.

3. Authenticate Earth Engine:

   • First, install the Earth Engine Python package:

     pip install earthengine-api

   • Then, run the following command to authenticate your account:

     earthengine authenticate

   • This will prompt you to visit a URL, sign in with your Google account, and authorize Earth Engine.

4. Set the Project ID:

   • In your Python code (specifically in Research_Platform.py or Discovery_Platform.py), you need to authenticate and set the project ID. Add the following snippet to your code:

     import ee
     ee.Initialize(project='your-project-id-here')

   • Replace your-project-id-here with your actual Google Cloud project ID.

5. Verify Authentication:

   • After authenticating, verify the setup by running:

     import ee
     ee.Initialize()

   If successful, you will be able to use Earth Engine for processing satellite data within the platform.

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Contributing

We welcome contributions! Please see the CONTRIBUTING.md file for guidelines on how to contribute to the platform.

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License

This project is licensed under the MIT License. See LICENSE for more details.

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