Sellards, P. K., & Allen, M., & Zhu, P., & Taylor, B. (2025, March), Robotic Wildfire Detection Using Computer Vision  Paper presented at 2025 ASEE North Central Section  (NCS) Annual Conference, Marshall University, Huntington, West Virginia. https://peer.asee.org/54685

\bibitem{asee_peer_54685}
  Sellards, P. K., \& Allen, M., \& Zhu, P., \& Taylor, B. (2025, March), \emph{Robotic Wildfire Detection Using Computer Vision}
  Paper presented at 2025 ASEE North Central Section  (NCS) Annual Conference, Marshall University, Huntington, West Virginia.
  https://peer.asee.org/54685

Preston K Sellards, Mathew Allen, Pingping Zhu, and Ben Taylor.  "Robotic Wildfire Detection Using Computer Vision".  2025 ASEE North Central Section  (NCS) Annual Conference, Marshall University, Huntington, West Virginia, 2025, March.  ASEE Conferences, 2025.  https://peer.asee.org/54685 Internet.  25 Apr, 2025

\bibitem{asee_peer_54685}
  Preston K Sellards, Mathew Allen, Pingping Zhu, and Ben Taylor.
  "Robotic Wildfire Detection Using Computer Vision".
  \emph{2025 ASEE North Central Section  (NCS) Annual Conference, Marshall University, Huntington, West Virginia, 2025, March}.
  ASEE Conferences, 2025.
  https://peer.asee.org/54685 Internet.  25 Apr, 2025

@INPROCEEDINGS{asee_peer_54685
author = "Preston K Sellards, Mathew Allen, Pingping Zhu, and Ben Taylor"
title = "Robotic Wildfire Detection Using Computer Vision"
booktitle = "2025 ASEE North Central Section  (NCS) Annual Conference"
year = "2025"
month = "March"
address = "Marshall University, Huntington, West Virginia"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/54685}
}

TY  - CPAPER
AB  - With the increasing development of artificial intelligence, its application in emergency response scenarios has shown promising potential for enhancing response times and mitigating damage. This paper explores the use of AI-driven drone swarms for rapid detection and response to wildfires, which pose escalating threats worldwide due to climate change. Leveraging autonomous swarm coordination, real-time data processing, and advanced image recognition, this approach demonstrates how AI-enabled drones can act as an early detection system and provide actionable data in volatile environments. By generalizing this application, the paper also highlights how similar AI-driven solutions could be adapted to respond to a range of natural disasters, offering a scalable, flexible, and resilient model for emergency management. Preliminary results indicate that such systems could significantly improve safety outcomes and resource efficiency, underscoring the transformative role of AI in future disaster response efforts.
AU  - Preston K Sellards
AU  - Mathew Allen
AU  - Pingping Zhu
AU  - Ben Taylor
CY  - Marshall University, Huntington, West Virginia
DA  - 2025/03/28
PB  - ASEE Conferences
TI  - Robotic Wildfire Detection Using Computer Vision
UR  - https://peer.asee.org/54685
ER  -