UC Berkeley

Deep learning has attracted much attention from the ecological community for its capability of extracting and generalizing patterns from data sets with highly complicated structures, such as images, audios, and motion signals. However, despite the promising cases, deep learning is complicated in terms of application and has shortcomings when applied to real-world ecological data sets. In this dissertation, we focus on: 1) demystifying the hidden mechanisms of deep learning in terms of wildlife recognition, 2) identifying the challenges of deep learning applications in wildlife recognition, and 3) proposing a generic recognition framework that can be practically deployed in the fields.

In the first chapter, we examine how deep learning recognizes wildlife through Convolutional Neural Network feature deconstruction and interpretation. The objective is to demystify aspects of artificial intelligence and facilitate wildlife recognition research.

The second chapter identifies three major challenges to automatic wildlife recognition through an avian recognition case study and provides preliminary solutions addressing each challenge. This chapter aims to increase awareness in the ecological community of these challenges, bridge the gap between ecological applications and state-of-the-art computer science, and open doors to future research.

In the third chapter, we propose a hybrid recognition system of machine learning and human-in-the-loop that overcomes two challenges discussed in the second chapter: imbalanced data distribution and continuous data expansion. Moreover, with the self-updating mechanism of our approach, the system can be practically deployed in the fields.