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Advanced solutions for wildlife management in developing nations

Organizer: Grant Hamilton
Format: Onsite-Online, Afternoon

Abstract:
The Earth is experiencing a mass extinction event with impending and major impacts on humans globally. Improved threatened species management is of critical international significance, and this hinges critically on effective monitoring. While there are ever more avenues for data collection, including a massive increase in the use of drones, it is improved analysis of the data that is collected that is at the heart of more effective conservation management. Machine learning for conservation is still in its infancy, and offers enormous potential for efficiency and accuracy gains in species detection and management. This is of profound importance since the majority of the earth’s biodiversity is found in the developing world and accurate and effective biodiversity monitoring in these areas is often limited by access to the necessary resources. This is a problem that inherently works across disciplines, with the need for expertise in ecology, engineering, and areas such as robotic vision and algorithm development. There is an urgent need to develop these effective and accurate analytical methodologies that can be implemented efficiently and that can lead to accurate monitoring outcomes across these areas. In this session, we call for presentations that demonstrate the effective use of machine learning and other advanced technological approaches in conservation. In particular, we seek those presentations that highlight the challenges, solutions to those challenges, and the highlights of working in diverse social, cultural and economic environments and ecosystems.

Themes: Knowledge-to-Action