Is this project an undergraduate, graduate, or faculty project?
Graduate
group
What campus are you from?
Daytona Beach
Authors' Class Standing
Bao Khoa Tran, Graduate Student
Lead Presenter's Name
Bao Khoa Tran
Faculty Mentor Name
Burak Canyaka
Abstract
Wildlife strikes pose a significant safety risk and economic burden to the aviation industry. Over 291,000 strikes have been reported in the U.S. since 1990, causing an estimated $248 million in annual losses. The Wild-AI (Wildlife Intelligence for Aviation Safety) project leverages cutting-edge technologies, including large language models (LLMs), machine learning (ML), explainable AI (XAI), and Unmanned Air Vehicles (UAV) imagery, to enhance wildlife hazard management at airports. Wild-AI transforms traditional Wildlife Hazard Assessments (WHAs) by integrating advanced data analytics and UAV monitoring. By utilizing historical wildlife-strike data and ML algorithms, we will identify key predictors of damaging strikes, such as flight
Did this research project receive funding support from the Office of Undergraduate Research.
No
The Wildlife Intelligence for Aviation Safety (Wild-AI)
Wildlife strikes pose a significant safety risk and economic burden to the aviation industry. Over 291,000 strikes have been reported in the U.S. since 1990, causing an estimated $248 million in annual losses. The Wild-AI (Wildlife Intelligence for Aviation Safety) project leverages cutting-edge technologies, including large language models (LLMs), machine learning (ML), explainable AI (XAI), and Unmanned Air Vehicles (UAV) imagery, to enhance wildlife hazard management at airports. Wild-AI transforms traditional Wildlife Hazard Assessments (WHAs) by integrating advanced data analytics and UAV monitoring. By utilizing historical wildlife-strike data and ML algorithms, we will identify key predictors of damaging strikes, such as flight