Grad Seminar: Towards Adaptable and Deployable Wildlife Detection from Aerial Imagery
Presenter
Jayden Hsiao, MASc candidate in Systems Design Engineering
Abstract
Accurate wildlife monitoring is critical for understanding ecosystem change, yet most conservation agencies still rely on manual interpretation of aerial imagery, a process that is slow, inconsistent, and difficult to scale. This talk presents two complementary advances aimed at making species detection from aerial platforms more adaptable and directly usable in real conservation workflows.
First, I introduce OpenWildlife, a multi-species open vocabulary detector trained across diverse aerial wildlife datasets. By using language guided grounding rather than fixed class labels, the model can identify a wide range of species including those not present in its training set and can be efficiently fine tuned with only a small number of annotated examples, giving conservationists a strong and broadly transferable starting point rather than requiring them to train a new model from scratch for each survey.
Second, I describe a deployed human in the loop annotation system built around this model and used operationally by the Arctic Eider Society for demographic analysis of eider duck populations in Hudson Bay. The workflow integrates automated predictions, regional correction tools, incremental fine tuning, and interface features designed for dense small object scenes typical of eider colonies, and in field use it substantially reduced annotation effort while enabling continuous model refinement as new imagery is collected.
Together, these components demonstrate a path toward wildlife detection systems that are both technically robust and practically deployable, supporting conservation programs that require adaptable and scalable tools rather than one off research models.