Monitor Elephant rumbles and gunshots using 24h audio recordings to help conservation efforts from researchers

Classifying and identifying bears on low-powered edge-hardware

Use Computer Vision to segment coral reefs in benthic imagery and measure long-term growth or loss of coral cover in marine protected areas

Develop computer vision software that can recognise and distinguish individual turtles through automated identification.

Classify the species and age of eagles to aid conservationists in monitoring their population health

Detect and classify the pelican population of the Danube Delta in Romania to evaluate the breeding population based on aerial photographs.

Build computer vision models to identify different species of European wildlife to improve population monitoring.

Detect elephant rumbles and gunshots on recordings made in the forests of central Africa and optimize the model to implement on-edge

Develop facial recognition CNN models for non-invasive study of harbor seals and other marine mammals, monitoring their population and movement patterns.

Using AI to help protect wildlife in South Africa. Developing an edge-ready computer vision model to detect poachers on thermal video streams on a fixed wing drone.

Creating a machine learning model for an on-edge detection of poachers on thermal video feed of a wildlife protection drone.

AI for Wildlife 3 is bringing poacher-detecting ML models into production on a wildlife protection drone. In the third challenge of the AI for Wildlife series we’ll be focusing on the elusive concept of MLOps.