MASTS Webinar: Towards Automated Cliff-Nesting Bird Monitoring using Machine Learning

MASTS is pleased to host a webinar featuring the work of Charis Hanna, a PhD student at the University of St Andrews.

Towards Automated Cliff-Nesting Bird Monitoring using Machine Learning

Cliff-nesting seabird colonies are difficult to survey manually at the frequency and scale needed for robust population monitoring. We present new machine learning methods for automated detection and counting of cliff-nesting birds, designed to scale to colony-level and population-wide monitoring without requiring new manual annotation for each site. We test these methods on imagery collected at the Isle of May, showing their potential as a practical tool for long-term seabird monitoring.

Charis is based at both the Scottish Oceans Institute and the School of Computer Science. Charis' work looks at developing novel deep learning approaches for the automated monitoring of dense cliff-nesting bird colonies. Her research focuses on advancing computer vision methods for detection, classification, and behavioural analysis in challenging habitats.

This session will not be recorded, but will feature a live Q&A with Charis after the presentation.