🌍 AI-Powered Amazon Rainforest Anomaly Detection

Hackathon Submission: Automated detection of deforestation and land-use changes in the Amazon rainforest using OpenAI GPT-4.1 vision models and satellite imagery.

Current Problem

Modis dataset is very coarse, it has a 500m accuracy. I need to get a much sharper dataset.

🎯 What It Does

This system automatically detects environmental changes in the Amazon rainforest by:

Fetching current satellite imagery from Google Earth Engine (Sentinel-2)
Comparing with historical data (MODIS 2020 baseline)
Using AI vision models to identify deforestation, construction, and land-use changes
Generating detailed reports with coordinates and analysis

⚡ Quick Demo

🚀 Key Features

AI-Powered Analysis: GPT-4.1 vision model analyzes satellite imagery
Real Satellite Data: Live Sentinel-2 imagery via Google Earth Engine
Automated Detection: Identifies deforestation, roads, and urban expansion
Geographic Validation: Analysis constrained to Amazon rainforest boundaries
Batch Processing: Analyzes multiple locations automatically

🛠️ Quick Start (5 Minutes)

Prerequisites

Python 3.12+
GitHub Token (free)
Google Earth Engine account (free)

1. Clone & Setup

git clone https://github.com/ShaafPlayz/OpenAI-to-Z-challenge.git
cd OpenAI-to-Z-challenge
pip install -r requirements-minimal.txt

2. Configure APIs

Create .env file:

GITHUB_TOKEN=your_github_token_here

3. Google Earth Engine Setup

earthengine authenticate
# Follow the browser authentication

4. Run the Analysis

jupyter notebook "GEE and OpenAI Research.ipynb"
# Open the notebook and run all cells

That's it! The system will automatically:

Generate random points in the Amazon rainforest
Fetch current satellite imagery
Compare with historical land cover data
Use AI to detect and report anomalies

📁 Project Structure

OpenAI-to-Z-challenge/
├── 📄 .env                                   # Environment variables (local)
├── � .gitignore                             # Git ignore rules
├── � README.md                              # Project documentation
├── � environment.yml                        # Conda environment configuration
├── � package-lock.json                      # Node.js dependencies lock file
├── �📝 requirements.txt                       # Full Python dependencies
├── 📝 requirements-minimal.txt               # Essential Python dependencies
├── 📓 GEE and OpenAI Research.ipynb          # Main research notebook
├── 📁 .git/                                 # Git repository data
├── 📁 .ipynb_checkpoints/                    # Jupyter notebook checkpoints
├── 📁 amazonia_boundary_proposal/              # Amazon boundary shapefiles
│   ├── 🗺️ amazonia_polygons.dbf              # Shapefile attribute data
│   ├── 🗺️ amazonia_polygons.prj              # Projection information
│   ├── 🗺️ amazonia_polygons.sbn              # Spatial index binary
│   ├── 🗺️ amazonia_polygons.sbx              # Spatial index
│   ├── 🗺️ amazonia_polygons.shp              # Main shapefile geometry
│   ├── 🗺️ amazonia_polygons.shp.xml          # Metadata
│   └── 🗺️ amazonia_polygons.shx              # Shapefile index
├── 📁 AppEEARS/                             # NASA AppEEARS MODIS datasets
│   ├── 🗺️ MCD12Q1.061_LC_Prop2_doy2020001_aid0001.tif  # MODIS 2020 land cover
│   ├── 🗺️ MCD12Q1.061_LC_Prop2_doy2021001_aid0001.tif  # MODIS 2021 land cover
│   ├── 🗺️ MCD12Q1.061_LC_Prop2_doy2022001_aid0001.tif  # MODIS 2022 land cover
│   ├── 🗺️ MCD12Q1.061_LC_Prop2_doy2023001_aid0001.tif  # MODIS 2023 land cover
│   ├── 🗺️ MCD12Q1.061_QC_doy2020001_aid0001.tif        # MODIS 2020 quality control
│   ├── 🗺️ MCD12Q1.061_QC_doy2021001_aid0001.tif        # MODIS 2021 quality control
│   ├── 🗺️ MCD12Q1.061_QC_doy2022001_aid0001.tif        # MODIS 2022 quality control
│   └── 🗺️ MCD12Q1.061_QC_doy2023001_aid0001.tif        # MODIS 2023 quality control
├── 📁 AppEEARSjpg/                          # Processed MODIS imagery (empty)
├── 📁 Resources/                            # Additional resources
│   ├── 🗂️ amazon_biome_border.zip          # Original biome shapefile data
│   └── 🗂️ amazonia_boundary_proposal_Eva_2005 (1).zip  # Boundary proposal data
├── 📁 satimagery/                           # Satellite imagery data
│   ├── 🖼️ sentinel_rgb.jpg                  # Processed RGB satellite image
│   └── 🗺️ sentinel_rgb.tif                  # Raw satellite data (GeoTIFF)
├── 📁 Technology Testing (Scripts)/          # Python scripts for testing
│   ├── 📄 .env                              # Local environment variables
│   ├── 🐍 GoogleEarthEngine.py              # Google Earth Engine integration
│   ├── 🔐 Offical_OpenAI_Key.py             # API key management
│   ├── 🤖 OpenAI-o3.py                      # OpenAI o3 model experiments
│   └── 🤖 OpenAI.py                         # Main OpenAI API implementation
└── 📁 WorkFlow Testing (Jupyter Notebooks)/ # Jupyter notebooks
    └── 📓 openai-research.ipynb              # Additional research notebook

� How It Works

The Analysis Pipeline

Boundary Validation: Ensures coordinates are within Amazon rainforest
Satellite Data Fetch: Downloads current Sentinel-2 imagery (10m resolution)
Historical Lookup: Retrieves MODIS 2020 land cover classification
AI Analysis: GPT-4.1 compares current vs. historical imagery
Anomaly Detection: Reports deforestation, construction, or land-use changes

Key Functions

find_and_log_anomalies(): Main detection pipeline
getAmazoniaAnalysisPoints(): Generates valid Amazon coordinates
get_modis_class_for_point(): Gets historical land cover data
promptGPT(): Sends imagery to AI for analysis

📊 Sample Output

Footprint ID: 1 at (-2.8234, -60.1234)
Historical (MODIS) Class: Evergreen Needleleaf Trees
Status: Anomaly Found - Construction activity detected

🛠️ Setup Instructions

Prerequisites

Python 3.12+
Conda (Anaconda/Miniconda)
GitHub Token (for GitHub Models access)
Google Earth Engine Account
OpenAI API Key (optional)

Environment Setup

Clone the Repository

git clone https://github.com/yourusername/OpenAI-to-Z-challenge.git
cd OpenAI-to-Z-challenge

Create and Activate Conda Environment

# Create the environment from YAML
conda env create -f environment.yml
conda activate OpenAI-GoogleEngine

# Or create manually
conda create -n OpenAI-GoogleEngine python=3.12
conda activate OpenAI-GoogleEngine

Install Dependencies

# Option 1: Install minimal requirements (recommended)
pip install -r requirements-minimal.txt

# Option 2: Install full environment
pip install -r requirements.txt

Environment Variables Create a .env file in the project root:

GITHUB_TOKEN=your_github_token_here
OPENAI_API_KEY=your_openai_api_key_here  # Optional

Google Earth Engine Authentication

# First time setup
earthengine authenticate

# Initialize with your project
# Replace 'your-project-id' with your actual GEE project ID

🔧 Usage

Main Research Notebook

# Launch Jupyter and open the main research notebook
jupyter notebook "GEE and OpenAI Research.ipynb"

Individual Components

OpenAI Integration

# Run OpenAI experiments
python "Technology Testing (Scripts)/OpenAI.py"

Google Earth Engine

# Test GEE functionality
python "Technology Testing (Scripts)/GoogleEarthEngine.py"

Satellite Imagery Analysis

The main workflow combines both GEE and OpenAI with external datasets:

Fetch Sentinel-2 satellite imagery from Amazon region via Google Earth Engine
Process and export imagery data to local storage formats
Convert to AI-readable format with brightness enhancement
Analyze using OpenAI vision models with geospatial analyst prompting
Generate natural language descriptions of surface features
Process MODIS land cover data from NASA AppEEARS
Generate statistical reports with AI-powered insights

Function-Based Workflow

The notebook includes modular functions for streamlined analysis:

getSatImageryGEE(): Fetch satellite data from coordinates
downloadSatIGEE(): Export imagery to local storage
GEE_to_brightJPG(): Convert and enhance images
prepareJPG_forOpenAI(): Encode for AI model consumption
getGEEImagery(): Complete pipeline from coordinates to AI-ready image
promptModel(): Multi-modal AI analysis with images
promptONLY(): Text-only AI analysis for reports

🌍 Example Analysis

The notebook demonstrates comprehensive analysis of the Amazon rainforest region near Manaus, Brazil:

Sentinel-2 Visual Analysis

Coordinates: -61.355873, -3.314660 (Manaus, Brazil)
Analysis Area: 7km buffer around coordinates
Data Period: June 2023 - June 2024
Image Resolution: 10m/pixel (Sentinel-2)
AI Model: OpenAI GPT-4.1 via GitHub Models

MODIS Land Cover Analysis

Dataset: MCD12Q1.061 Land Cover Type (2020-2023 time series)
Classification: Plant Functional Type (PFT) classes
Quality Control: Dedicated QC datasets for each year
Analysis: Pixel-level land cover statistics and temporal changes
Geospatial Context: Amazon biome boundary integration
Output: Automated markdown reports with multi-year insights

Analysis Results

The system can identify and quantify:

Evergreen Needleleaf Trees: Primary forest coverage
Deciduous Needleleaf Trees: Seasonal vegetation
Flooded Vegetation: Wetland and riverine areas
Urban Areas: Human settlement detection
Unknown/No Data: Data quality assessment

� Tech Stack

AI Model: OpenAI GPT-4.1 (via GitHub Models API)
Satellite Data: Google Earth Engine (Sentinel-2)
Historical Data: NASA MODIS Land Cover (2020-2023)
Languages: Python, Jupyter Notebooks
Key Libraries: openai, earthengine-api, geopandas, rasterio

🎯 Impact

This system can help:

Environmental Organizations: Monitor deforestation in real-time
Government Agencies: Track illegal logging and land-use violations
Research Institutions: Study climate change impacts
Conservation Efforts: Protect endangered ecosystems

Ready to try it? Just run the Quick Start above! 🚀

Hackathon Submission - June, 2025

Name		Name	Last commit message	Last commit date
Latest commit History 18 Commits
.ipynb_checkpoints		.ipynb_checkpoints
AppEEARS		AppEEARS
Resources		Resources
Technology Testing (Scripts)		Technology Testing (Scripts)
WorkFlow Testing (Jupyter Notebooks)		WorkFlow Testing (Jupyter Notebooks)
amazon_biome_border		amazon_biome_border
amazonia_boundary_proposal		amazonia_boundary_proposal
satimagery		satimagery
.gitignore		.gitignore
GEE and OpenAI Research.ipynb		GEE and OpenAI Research.ipynb
README.md		README.md
environment.yml		environment.yml
package-lock.json		package-lock.json
requirements.txt		requirements.txt

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🌍 AI-Powered Amazon Rainforest Anomaly Detection

Current Problem

🎯 What It Does

⚡ Quick Demo

🚀 Key Features

🛠️ Quick Start (5 Minutes)

Prerequisites

1. Clone & Setup

2. Configure APIs

3. Google Earth Engine Setup

4. Run the Analysis

📁 Project Structure

� How It Works

The Analysis Pipeline

Key Functions

📊 Sample Output

🛠️ Setup Instructions

Prerequisites

Environment Setup

🔧 Usage

Main Research Notebook

Individual Components

OpenAI Integration

Google Earth Engine

Satellite Imagery Analysis

Function-Based Workflow

🌍 Example Analysis

Sentinel-2 Visual Analysis

MODIS Land Cover Analysis

Analysis Results

� Tech Stack

🎯 Impact

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