Grouper Spotter - Citizen science and facial recognition for grouper

Machine Learning & Citizen Science & Conservation Research

Grouper Spotter applies computer vision algorithms and deep learning to identify and track individual grouper 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 grouper 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 body coloration or fin edges. 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.

One Platform, Many Species, Many Researchers

We can identify individuals of these species using fully automated computer vision:

6 identified groupers

648 reported sightings

21 citizen scientists

7 researchers and volunteers