Machine Learning & Citizen Science & Conservation Research

Flukebook applies computer vision algorithms and deep learning to identify and track individual whales and dolphins 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 whales and dolphins 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:

Megaptera novaengliae (humpback whales) can be identified by the contrasting color and the trailing edge of their flukes using three algorithms: HotSpotter, CurvRank, and OC/WDTW

Physeter macrocephalus (sperm whales) can be identified by the trailing edge of their flukes using two algorithms: CurvRank and OC/WDTW

Tursiops truncatus(bottlenose dolphins) can be identified by the trailing edge of their fins with the CurvRank algorithm.

Eubalaena spp.(right whales) can be identified by the contrasting color on their heads by the HotSpotter and DeepSense.ai algorithms.

and more soon!

Why we do this