EU Fire Analysis v1.4.2

Europe-focused Forest Fire Detection & Analysis System using NASA FIRMS Data

🌍 Overview

EU Fire Analysis v1.4.2 is a dedicated system for detecting and analyzing forest fires across Europe using NASA FIRMS (Fire Information for Resource Management System) data. By integrating cutting-edge AI techniques with geospatial analysis, it enables detailed pattern recognition of fire activity.

🎯 Key Features

Geographic Coverage

• Scope: Entire European region (34°N–72°N, 25°W–50°E)
• Regional Breakdown: Over 12 detailed subregions
  • Nordic: Scandinavia, North Atlantic
  • Western Europe: British Isles, Continental West, Central West
  • Southern Europe: Iberian Peninsula, Western Mediterranean, Balkans
  • Eastern Europe: Central, Black Sea, Western Russia

Technical Specifications

• AI Embedding: Sentence Transformers (all-MiniLM-L6-v2)
• Clustering: FAISS k-means (optimized for large-scale data)
• Visualization: t-SNE, regional heatmaps, temporal analysis
• GPU Acceleration: CUDA-enabled (if available)

🚀 Quick Start

1. Environment Setup

# Install dependencies
pip install -r requirements.txt

# Recommended for GPU environments
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

2. Configuration File

Adjust parameters in config_europe_firms.json:

{
  "region": "Europe",
  "coordinates": {
    "south": 34.0, "north": 72.0,
    "west": -25.0, "east": 50.0
  },
  "max_samples": 15000,
  "days_back": 10
}

3. Run Analysis

python europe_firms_pipeline_v2.py

📊 Analysis Results

Performance Metrics (Latest Run)

• Processing Time: 76.45 seconds (13,334 samples)
• Quality Score: 0.648
• Number of Clusters: 15
• Coverage: Full EU region

Output Files

1. 📊 Data Files

   • nasa_firms_data.csv – Raw FIRMS data
   • europe_fires_clustered.csv – Clustered results
   • final_europe_results.json – Summary of analysis

2. 🖼️ Visualization Files

   • tsne_plot.png – Cluster distribution
   • cluster_regional_analysis.png – Regional breakdown
   • cluster_geographic_distribution.png – Geographic spread
   • cluster_intensity_analysis.png – Fire intensity
   • cluster_temporal_patterns.png – Temporal trends

3. 📝 Report

   • comprehensive_fire_analysis_report.md – Full analytical report

🔧 Core Components

europe_firms_pipeline_v2.py

Main pipeline: data collection → embedding → clustering → visualization

cluster_feature_analyzer.py

Regional analysis engine:

• Detailed classification of 12+ European regions
• Geographic, temporal, and intensity pattern analysis
• Multidimensional visualizations

scripts/

• data_collector.py – NASA FIRMS API integration
• embedding_generator.py – Embedding generation
• clustering.py – FAISS k-means implementation
• visualization.py – Visualization engine

🌟 Advanced Features

Detailed Regional Classification

Expanded from 2 basic zones to 12+ fine-grained regions:

Nordic Region

• Nordic (Scandinavia): Norway, Sweden, Finland
• Nordic (North Atlantic): Iceland, Faroe Islands

Western Europe

• British Isles: UK, Ireland
• Western Europe (Continental): France, Belgium, Netherlands
• Central Western Europe: Germany, Switzerland

Southern Europe

• Iberian Peninsula: Spain, Portugal
• Mediterranean West: Southern France, Italy
• Balkans: Balkan Peninsula
• Southeast Mediterranean: Greece, Southern Balkans

Eastern Europe

• Central Europe: Poland, Czech Republic
• Eastern Europe (Central): Ukraine, Belarus
• Eastern Europe (Russia): Western Russia
• Eastern Europe (Black Sea): Black Sea coast

Adaptive Clustering

• Automatically selects optimal method based on data size
• HDBSCAN (small-scale) → FAISS k-means (large-scale)
• Real-time quality evaluation

📈 Use Cases

1. Fire Monitoring & Early Warning

Real-time surveillance of fire activity across Europe

2. Regional Fire Pattern Analysis

Detailed insights into seasonal trends, intensity, and frequency

3. Research & Policy Support

Supports evidence-based disaster prevention and environmental policy

4. International Collaboration

Facilitates fire data sharing and coordinated response among EU nations

🔧 Configuration Options

Custom Coordinate Range

Focus on specific regions:

{
  "coordinates": {
    "south": 50.0, "north": 60.0,  // Nordic only
    "west": 0.0, "east": 20.0
  }
}

Time Range Adjustment

{
  "days_back": 30,  // Last 30 days
  "max_samples": 50000  // Sample limit
}

📊 Sample Results

Latest Analysis (Run on September 15, 2025)

• Total Fires Detected: 13,334
• High-Confidence Detections: 13,334 (≥50% confidence)
• Regional Distribution:
  • Mediterranean: 43.6% (5,816)
  • Eastern Europe: 62.8% (8,382)
  • Western Europe: 15.3% (2,040)
  • Nordic: 1.5% (197)
• Processing Time: 76.45 seconds
• Quality Score: 0.648

Geographic Characteristics

• Latitude Range: 34.0°N – 66.3°N
• Longitude Range: -22.3°W – 50.0°E
• Density: High concentration along Mediterranean coast and Balkans

🛠️ Technical Requirements

Minimum Requirements

• Python 3.8+
• RAM: 8GB+
• Storage: 10GB+

Recommended Environment

• Python 3.9+
• RAM: 16GB+
• GPU: CUDA-enabled (NVIDIA GTX 1660 or higher)
• Storage: SSD 20GB+

Dependencies

torch>=1.9.0
transformers>=4.20.0
sentence-transformers>=2.2.0
faiss-cpu>=1.7.0  # or faiss-gpu
numpy>=1.21.0
pandas>=1.3.0
matplotlib>=3.5.0
seaborn>=0.11.0
scikit-learn>=1.0.0

📄 License

MIT License – See LICENSE file for details

🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📞 Support

• Issues: GitHub Issues
• https://www.linkedin.com/in/yasunotkt/

🔄 Version History

v1.4.2 (Current)

• ✅ Detailed regional classification (12+ zones)
• ✅ FAISS k-means optimization
• ✅ GPU acceleration support
• ✅ Comprehensive visualization system
• ✅ Real-time quality evaluation

Previous Versions

• v1.3: Asia-Pacific Fire Analysis
• v1.4: South America Fire Analysis

---

EU Fire Analysis v1.4.2 – Europe-specific forest fire analysis system
Powered by NASA FIRMS Data & Advanced AI Technology