Concordia University

Fellowship Description

Research program title

Development on Forests Surveillance and Fires Detection Using Multiple Heterogeneous Unmanned Systems and Remote Sensing Techniques

Reference number



Program description

Every year, thousands of people are injured/killed and millions of hectares of forests are destroyed by forest fires and billions of dollars are spent to extinguish these fires. Early detection and suppression of forest fires are crucial to minimize the destructions that fires may cause. Because of their rapid maneuverability, extended operational range, and improved personnel safety, a group of heterogeneous unmanned systems (USs) (including robotic systems, unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), unmanned surface/underwater vehicles (USVs/UUVs), and satellites) along with remote sensing techniques can be properly employed for forest surveillance and fire detection and fighting. The main aims and objectives of this research project are to develop a novel and advanced artificial intelligent (AI) approach by commanding numerous USs with remote sensing sensors for monitoring forests, detecting forest fires/other uncertainties and risks (caused by drought, disease, hurricane, ice, flooding, etc.), tracking them, and predicting their time and range evolution autonomously or with minimum human interference. The knowledge (remote sensing based surveillance and detection methods, and cooperation of multiple heterogeneous USs) gained in this research will be transferable and also find a variety of other applications, such as precision agriculture, forests (health, diversity, etc.) surveillance and management, pipeline/powerline monitoring, wind farms and solar panels array surveillance, as well as natural resources and environment surveillance and protection, all of which are important to significantly spur Canada's transition to smarter, safer, stronger, more efficient and reliable environment protection and management with great adaptability and sustainability.

Academic qualifications required

PhD in Mechanical Engineering with experiences in both unmanned systems control and image processing researches.

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