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Wildfire Projects

This repository contains a comprehensive collection of wildfire-related research, modeling, and data analysis projects. All wildfire work has been organized into a single, structured repository for better management and collaboration.

📁 Project Structure

🔥 fire-pipe/

A comprehensive wildfire data processing and analysis pipeline with machine learning models for fire prediction and spreading analysis.

Key Features:

  • Data ingestion and processing workflows
  • Machine learning models (including FireBench integration)
  • Visualization tools
  • Downscaling capabilities
  • Complete pipeline automation

Contents:

  • data/ - Raw and processed wildfire datasets
  • models/ - ML models and training scripts
  • ingest/ - Data ingestion modules
  • scripts/ - Utility and automation scripts
  • viz/ - Visualization components
  • downscale/ - Data downscaling tools
  • features/ - Feature engineering modules
  • wildfire_data_fetcher/ - Data fetching utilities

📊 wildfire_data/

Core wildfire data management and API infrastructure for accessing and processing wildfire datasets.

Key Features:

  • API endpoints for wildfire data access
  • Configuration management for different data sources
  • Spatial data processing
  • Core data handling utilities
  • Quality assurance modules

Contents:

  • api.py - Main API interface
  • config/ - Configuration files
  • core/ - Core data processing modules
  • drivers/ - Database and storage drivers
  • plugins/ - Extensible plugin system
  • qa/ - Quality assurance tools
  • spatial/ - Spatial data handling

📈 spreading_model/

Advanced wildfire spreading models using physics-informed neural networks (PINNs) and other machine learning approaches.

Key Features:

  • Physics-informed neural network implementations
  • FireBench PINN data integration
  • Advanced spreading algorithms
  • Model training and evaluation

Contents:

  • src/ - Source code for spreading models
  • src/firebench_pinn_data/ - FireBench PINN data handling
  • requirements.txt - Python dependencies
  • Git-tracked development history

🌊 wildfire_spreading/

Beam pipeline implementation for wildfire spreading analysis and gradient computation.

Key Features:

  • Apache Beam pipeline for distributed processing
  • Gradient computation for spreading analysis
  • Scalable data processing workflows

Contents:

  • beam_pipeline/ - Apache Beam pipeline components
  • fetch_gradients.py - Gradient computation utilities

🚀 Getting Started

Prerequisites

  • Python 3.8+
  • Git
  • (Optional) Apache Beam for distributed processing
  • (Optional) Docker for containerized environments

Installation

  1. Clone the repository:
git clone https://github.com/modorethegreat/Wildfire-Projects.git
cd Wildfire-Projects
  1. Install project dependencies:
# Install core dependencies
pip install -r wildfire_data/requirements.txt

# Install spreading model dependencies
pip install -r spreading_model/requirements.txt

# Install fire-pipe dependencies
pip install -r fire-pipe/requirements.txt
  1. Set up environment variables:
# Copy example configuration files and modify as needed
cp wildfire_data/config/example.yaml wildfire_data/config/local.yaml

Quick Start Examples

Data Access via API

from wildfire_data.api import WildfireAPI

# Initialize API
api = WildfireAPI()

# Fetch recent wildfire data
data = api.get_recent_wildfires(days=7)
print(f"Found {len(data)} wildfire incidents")

Run Spreading Model

cd spreading_model
python src/run_model.py --config configs/default.yaml

Execute Fire-Pipe Pipeline

cd fire-pipe
python scripts/run_pipeline.py --mode ingestion --source api

📊 Project Overview

Project Primary Focus Technology Stack Status
fire-pipe Complete data pipeline Python, ML, Apache Beam ✅ Active
wildfire_data Data management & API Python, FastAPI, Spatial ✅ Active
spreading_model ML spreading models Python, PyTorch, PINNs ✅ Active
wildfire_spreading Beam processing Python, Apache Beam ✅ Active

🔧 Technical Features

Data Processing

  • Multi-source data ingestion from various wildfire databases
  • Spatial data handling with GIS integration
  • Real-time data fetching with automated updates
  • Quality assurance pipelines for data validation

Machine Learning

  • Physics-informed neural networks for spreading prediction
  • FireBench integration for standardized evaluation
  • Feature engineering pipelines optimized for wildfire data
  • Model training and evaluation frameworks

Scalability

  • Apache Beam pipelines for distributed processing
  • Modular architecture allowing independent component scaling
  • Container-ready deployment options
  • Cloud integration capabilities

📈 Research Applications

This codebase supports various wildfire research applications:

  • Wildfire spreading prediction using ML and physics-based models
  • Historical analysis of wildfire patterns and trends
  • Real-time monitoring and early warning systems
  • Risk assessment and modeling for vulnerable areas
  • Climate impact studies on wildfire behavior

🤝 Contributing

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

📝 Documentation

Each subproject contains its own detailed documentation:

  • Check individual README.md files in each directory
  • API documentation in wildfire_data/
  • Model documentation in spreading_model/
  • Pipeline documentation in fire-pipe/

📄 License

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

🔗 Related Projects

  • FireBench - Wildfire benchmarking suite
  • Apache Beam - Unified model for defining both batch and streaming data-parallel processing pipelines

📞 Contact

For questions or collaboration opportunities, please open an issue in this repository.

Last Updated: January 2026
Organization: Consolidated from multiple wildfire research projects