Welcome to Wildbook for Zebras!

Machine Learning & Citizen Science & Conservation Research

Wildbook for Zebras applies computer vision algorithms and deep learning to identify and track individual zebras across hundreds of thousands of photos. We help researchers collaborate with each other and citizen scientists contribute to the effort. A.I. scales and speeds research and conservation.

Step 1. Deep Learning Finds Animals

We train computer vision to find individual zebras in photos and identify the species.

Step 2. Algorithms and Neural Networks Identify Individuals

When we know where each animal is, we can identify them individually using algorithms that make digital "fingerprints" for each animal, such as identifying them by their unique stripes. We replace hours of human labor with just a few minutes of computer vision, scanning for matches across tens of thousands of photos.

Step 3. Population Dynamics Define Conservation Action

If we can quickly track individuals in a population, we can model size and migration to generate new insights and support rapid, data-driven conservation action.

5210 identified Plains Zebras

3453 identified Grevy's Zebras

32000 reported sightings