Abstract: Monitoring biodiversity at scale is one of the most critical challenges in ecology and conservation today. Traditional methods of field surveys and manual observation, while valuable, are often limited in scope, time, and resources. Automated sensing technologies, combined with advances in artificial intelligence, offer a transformative pathway to assess biodiversity across diverse ecosystems with unprecedented efficiency.
In this talk, Aditya Jain from the Quebec AI Institute will present recent developments in automated sensors designed for scalable biodiversity assessment. The session will explore how sensor networks, machine learning algorithms, and edge computing can be integrated to collect, process, and analyze ecological data in real time.
Key themes will include:
The role of automated sensors in capturing species presence, abundance, and behavior.
How AI-driven models can classify and interpret ecological signals such as audio, video, and environmental data.
Challenges of deploying sensor systems in remote and dynamic environments.
Applications in conservation planning, ecosystem monitoring, and climate change research.
The presentation will highlight case studies demonstrating how automated sensing can bridge the gap between large-scale data collection and actionable ecological insights. By combining cutting-edge AI with environmental science, this approach promises to make biodiversity assessment more scalable, accurate, and impactful in addressing global sustainability challenges.