Deep Learning and Synthetic Imagery for Migratory Bird Species Identification Using Drones

Abstract

Recent technology advancements have caused a significant increase in popularity and availability of uninhabited aerial systems (UAS). Commercially available platforms are often equipped with powerful sensors, and have become a tool for researchers in many fields. In ornithology, using drones for surveying and population monitoring of different bird species is a relatively lower cost and higher time efficiency approach, when compared to ground based alternatives. In this thesis work, the use of UAS in the study of migratory shorebird species in Canada is explored with the development of computer vision applications. More specifically, a deep learning classification model is trained to identify the presence of birds of a given species in an image. The species selected for the first iteration of the application is the Canada geese, which is not one of the migratory shorebird species of interest, but is used as a proxy due to its availability and similar melanin colours. Images