  Wildlife monitoring is crucial to nature conservation and has been done by
manual observations from motion-triggered camera traps deployed in the field.
Widespread adoption of such in-situ sensors has resulted in unprecedented data
volumes being collected over the last decade. A significant challenge exists to
process and reliably identify what is in these images efficiently. Advances in
computer vision are poised to provide effective solutions with custom AI models
built to automatically identify images of interest and label the species in
them. Here we outline the data unification effort for the Wildlife Insights
platform from various conservation partners, and the challenges involved. Then
we present an initial deep convolutional neural network model, trained on 2.9M
images across 465 fine-grained species, with a goal to reduce the load on human
experts to classify species in images manually. The long-term goal is to enable
scientists to make conservation recommendations from near real-time analysis of
species abundance and population health.
