wildfire-alphaearth-sentinel

This MVP demonstrates a multi-indicator, high-reliability wildfire detection framework that surpasses conventional approaches. By combining Earth observation with intelligent vector analytics, it opens pathways to operational-scale environmental monitoring.

🔥 AlphaEarth Wildfire Detection MVP

A high-precision wildfire detection and differential analysis MVP system. This project combines Google Earth Engine and AlphaEarth embedding technologies to capture multi-period satellite patterns and anticipate fire-related anomalies.

📦 Overview

This repository implements a three-stage wildfire detection pipeline:

1. Satellite Image Collection & Preprocessing (Google Earth Engine)
2. High-dimensional Embedding Vector Generation (AlphaEarth Foundations)
3. Multifaceted Similarity & Anomaly Analysis (Statistics + ML)

Target Area: 2017 California Thomas Fire (Ventura County)

🚀 System Structure

📡 Satellite Image Collection

• Source: Sentinel-2 multispectral satellite
• Resolution: 10m
• Bands: RGB + NIR
• Periods: Pre-, During-, and Post-fire timeframes
• Processing: Cloud masking, atmospheric correction, normalization

🔎 Embedding Generation

• Interface: AlphaEarth Foundations API
• Format: RGB image → 512D normalized vector
• Simulation fallback: Gaussian distribution for API throttling

🧠 Similarity & Anomaly Analysis

• Similarity Metrics: Cosine, Pearson, Spearman
• Distance Metrics: Euclidean, Manhattan, Chebyshev
• Temporal Change Analysis: Magnitude, velocity, directional shift
• Anomaly Detection: Isolation Forest, LOF, Z-score statistics
• Clustering: K-means, Hierarchical clustering
• PCA-based fire pattern extraction

🧪 Execution

🔧 Required Libraries

pip install earthengine-api numpy pandas matplotlib seaborn
pip install scikit-learn scipy requests folium

🏁 Run Instructions

jupyter notebook wildfire_alphaearth_Mvp.ipynb

• Execute notebook cells sequentially
• Final dashboard displays detection results and recommended actions

📊 Output Format

🔥 Fire Probability: 0.8731 (87.31%)
⚠️ Risk Level: HIGH
📝 Decision Result: High fire possibility
🎯 Confidence: HIGH
💡 Recommended Action: Regional emergency response advised

🌍 Expansion Roadmap

• Real-time streaming support for live fire detection
• Global wildfire monitoring network
• Deep learning integration for predictive analytics
• System integration with disaster response and evacuation platforms

🏆 Project Significance

This MVP demonstrates a multi-indicator, high-reliability wildfire detection framework that surpasses conventional approaches. By combining Earth observation with intelligent vector analytics, it opens pathways to operational-scale environmental monitoring.

📬 Contribution & Support

• Use GitHub Issues for bug reports and feature requests
• Pull Requests are welcome!