As camera traps have grown in popularity, their utilization has expanded to numerous fields, including wildlife research, conservation, and ecological studies. The information gathered using this equipment gives researchers a precise and comprehensive understanding about the activities of animals in their natural environments. For this type of data to be useful, camera trap images must be labeled so that the species in the images can be classified and counted. This has typically been done by teams of researchers and volunteers, and it can be said that the process is at best time-consuming. With recent developments in deep learning, the process of automatically detecting and identifying wildlife using Convolutional Neural Networks (CNN) can significantly reduce the workload of research teams and free up resources so that researchers can focus on the aspects of conservation.
Level of Degree
First Committee Member (Chair)
Second Committee Member
Third Committee Member
George Matthew Fricke
Transfer Learning, Deep Learning, Convolutional Neural Network, Conservation, Sevilleta, Data Augmentation
Gurule, Michael. "The Impacts of Transfer Learning for Ungulate Recognition at Sevilleta National Wildlife Refuge." (2023). https://digitalrepository.unm.edu/geog_etds/65