Near Real-Time Multi-Sensor Geospatial Data Processing Using Quantum Machine Learning for Forest Fire Hazard Mitigation

Forest fire hazard mitigation is crucial for Canada due to its vast and diverse forests, which are highly prone to wildfires. These fires threaten human lives, property, and wildlife, and can cause serious economic and environmental damage. Effective strategies can reduce the frequency, intensity, and spread of wildfires, protecting communities, preserving valuable timber, and maintaining biodiversity. This project aims to improve fire hazard mitigation by using advanced multi-sensor data and quantum machine learning (QML) to detect, monitor, and predict fires more accurately. Additionally, this project will enhance the understanding of the potential of QML to improve fire hazard mitigation and its applications to other remote sensing uses for students and faculty members of the University of New Brunswick, Canada and Rajiv Gandhi Institute of Petroleum Technology, India. These proactive measures are essential for safeguarding Canada’s natural heritage and ensuring the safety and well-being of its residents.

Faculty Supervisor:

Rakesh Mishra

Student:

Partner:

Rajiv Gandhi Institute of Petroleum Technology

Discipline:

Earth science

Sector:

Education

University:

University of New Brunswick

Program: