Wildfire Emergency Response and Evacuation Framework Using Drones: Phase I

The scorching wildfires of 2017 and 2018 cast California into a devastating inferno, seizing national attention and leaving entire communities in ruins. The ferocious Thomas fire, tearing through Ventura and Santa Barbara Counties, and the relentless Tubbs fire, laying waste to Napa, Sonoma, and Lake Counties, unleashed destruction upon more than 7,200 structures and devoured a staggering 318,000 acres in 2017. Then, in 2018, the Woolsey fire's unforgiving blaze scarred 1,990 structures across nearly 97,000 acres in Los Angeles and Ventura Counties. The state faced a historic wildfire season in 2020, including the August Complex Fire, which surpassed the Mendocino Complex as the largest recorded wildfire in California's history.

These wildfires not only resulted in substantial property damage and loss of life but also had severe environmental impacts, affecting air quality, wildlife, and ecosystems across the state. The scale and frequency of these wildfires highlight the urgency for innovative approaches to wildfire prevention, early detection, and efficient response strategies to mitigate future catastrophes.

Several research works have been conducted on wildfire detection, spread estimation, wildfire evacuation, and search and rescue operations. However, to the best of our knowledge, there is no unified framework that tries to address several of those important issues simultaneously in a single framework. We are confident that implementing our proposed framework would significantly benefit wildfire control authorities.

In this project, we propose to develop a unified framework for wildfire emergency response and evacuation that includes:
•    Dynamic Environment Modeling: Utilizing machine learning algorithms coupled with infrared sensors, visible cameras, and weather sensors including temperature, wind direction, and speed detection mounted on a drone to predict forest fires before they occur.
•    Evacuation Route Optimization: Leveraging a drone equipped with machine learning algorithms to optimize path planning during wildfire emergencies, enabling efficient and safe evacuation routes while aiding first responders in containment efforts.
•    Search and Rescue Operations: Employing the framework to identify and locate firefighters trapped in wildfire, especially when communication links with the base station are lost.

Principal Investigator: 
Hovannes Kulhandjian
PI Contact Information: 


California State University, Fresno

January 2024 to December 2024
Implementation of Research Outcomes: 

The anticipated outputs from this research project encompass technological advancements, innovative processes, and valuable datasets contributing to the development of a comprehensive wildfire response system. These outputs aim to significantly benefit wildfire control authorities by enhancing their capabilities in prevention, early detection, and efficient emergency response strategies, ultimately mitigating the impact of future wildfire catastrophes.

Impacts/Benefits of Implementation: 

The implementation of the proposed unified framework for wildfire emergency response and evacuation is expected to bring about significant positive impacts on the transportation system, regulatory frameworks, and overall wildfire management practices. The anticipated benefits include:

  • Enhanced Emergency Response:
    • Safety - Early detection and prediction of forest fires through dynamic environment modeling using advanced sensor technology and machine learning algorithms will enable proactive responses, improving the safety of affected communities and first responders.
    • Reliability - Real-time wildfire data integration will enhance the reliability of emergency response efforts, ensuring timely and informed decision-making.
  • Efficient Evacuation Routes:
    • Safety and Reliability - The utilization of machine learning-driven algorithms for evacuation route optimization on drones will lead to more efficient and safe evacuation routes. This not only ensures the safety of evacuees but also aids first responders in containment efforts, minimizing the risk of transportation-related accidents during evacuations.
    • Search and Rescue Operations: Durability: The proposed framework for search and rescue operations, especially in scenarios with communication breakdowns, will enhance the durability of emergency response efforts. Improved identification and location capabilities will contribute to the safety and well-being of trapped firefighters.
  • Positive Environmental Impact:
    • Costs: While not explicitly mentioned, the reduction in the scale and impact of wildfires through early detection and efficient response strategies is likely to result in cost savings related to property damage, environmental rehabilitation, and firefighting efforts.
  • Policy and Regulatory Impact:
    • Innovation: The development of a unified framework addressing multiple wildfire-related challenges simultaneously may lead to the introduction of innovative practices and technologies in wildfire management, influencing policy decisions.
      Legislative Considerations: The successful implementation of the proposed framework may prompt legislative considerations for incorporating advanced technologies and methodologies into statewide wildfire management policies.
  • Technological Advancements: 
    • Products and Patents: The integration of infrared cameras, visible cameras, and weather sensors on drones for dynamic environment modeling may result in new technologies or products. This could lead to invention disclosures or patent filings, potentially contributing to advancements in the field.

In the subsequent phase of this project, our goal is to deploy the system within wildfire emergency protocols. This deployment involves seamless integration with emergency communication systems and collaboration with local authorities. Our next steps include engaging with fire authorities to coordinate real-time testing and drone operations, ensuring the system's practical implementation and effectiveness.

Project Number: 



Contact Us

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