Sea lion Counting from Aerial Images with Deep Learning : A density Map Approach

The ability to automatically count animals may be essential for their survival. Out of all living mammals on Earth 60% are livestock, 36% humans, and only 4% are animals that live in the wild. In a relatively short period, human development of civilization caused a loss of 83% of all wildlife and 50% of all plants. The rate of species extinctions is accelerating. Wildlife surveys provide a population estimate and are conducted for various reasons such as species management, biology studies, and long term trend monitoring. In this thesis, we propose the use of deep learning (DL), together with satellite imagery, to count the numbers of sea lions with high precision. The proposed approach shows promising results than the state-of-art DL models used for counting, indicating that proposed method has the potential to be used more widely in large-scale wildlife surveying projects and initiatives.