The Problem

The escalating crisis of biodiversity loss presents a formidable challenge, with species disappearing at an unprecedented rate due to habitat destruction and climate change. Traditional wildlife monitoring methods need to be improved for the comprehensive, non-intrusive, and long-term data collection needed for effective conservation efforts, highlighting a critical gap in our ability to protect and understand our planet's diverse species.

Our Approach

Our approach to wildlife monitoring combines AI-driven facial recognition with advanced machine-learning techniques. In the first phase, we use algorithms to accurately extract animal faces from various images, ensuring clear and focused data for analysis. The second phase takes these extracted faces and applies metric learning, a key aspect that enables our AI models to recognize and learn the unique features of each species. This process is dynamic; as the model encounters more data, it continuously improves its ability to distinguish between individual animals, adapting to changes in their appearance due to aging, seasonal variations, or other environmental factors. This evolving capability of our models to recognize new individuals over time is crucial, ensuring long-term effectiveness and keeping pace with the ever-changing dynamics of wildlife populations.