Learning to Compare: A Visual Guide to Metric Learning for Wildlife Re-ID
An intuition-first, visual guide to metric learning — how machines learn to tell individual animals apart for conservation re-identification.
Stories from the field and under the hood — how open technology helps protect wildlife and the planet.
An intuition-first, visual guide to metric learning — how machines learn to tell individual animals apart for conservation re-identification.
How a literature survey, self-contained experiments, and a shared leaderboard turned 28 research papers into a production smoke verifier with 4x fewer false alarms.
How the temporal smoke verifier works, step by step — detection boxes become tubes, stabilized crops make motion legible, and a vision transformer plus a tiny temporal head learn that smoke grows and drifts.
Our experiences at various Wildlife Rescue Centers
A comprehensive examination of using local feature matching for individual identification.
An in-depth look at the common preprocessing stages required to perform identification using computer vision.
An in-depth look at the systems developed and deployed to track the journey of wild salmon as they return to their natal streams.
Detecting early forest fires in real time using low powered technology
Learn how to leverage audio processing to localize elephant rumbles on terabytes of data.
Identify bears with Metric Learning.
Detecting bears in real time using low-power technology.
Learn how to successfully train a computer vision model to accurately analyse underwater benthic imagery.
Detecting bears in real time using low-power technology.