Snow Leopard Identification

Identifying individual snow leopards by their coat patterns to support conservation across Central Asia.

Manual

Upload Snow Leopard Image

Select an image of a snow leopard showing distinctive coat patterns. The system works with multiple body parts including head, flanks, and tail.
Configure Matching

Optionally filter by location and body part, then click 'Submit' to process the image.
Review Matches

The system displays the top matching individuals from the catalog with confidence scores. High confidence matches (🔵 Excellent, 🟢 Good) indicate likely the same individual.

Overview

The Snow Leopard Identification system lets researchers and conservationists track individual snow leopards across Central Asia’s mountain ranges without invasive tagging. By analyzing the unique rosette patterns on each animal’s coat, it builds distinctive visual fingerprints that stay stable throughout the animal’s life.

Snow leopards are notoriously hard to study — their remote, high-altitude habitats span rugged terrain across 12 countries. This technology turns camera trap images into actionable conservation data, supporting individual identification across survey seasons, population estimation in remote regions, movement tracking between territories, and long-term survival monitoring.

Key Features

What the identification system brings to the field:

Multi-body-part matching

Works with images of head, left flank, right flank, tail, and other body regions — so you can identify an individual from whatever the camera captured.

Location filtering

Narrow catalog searches by geographic region to speed up matching and cut down false candidates.

Multiple feature extractors

Choose between SIFT, SuperPoint, DISK, or ALIKED to get the best matching performance for your imagery.

Confidence scoring

A four-level confidence system — 🔵 Excellent, 🟢 Good, 🟡 Fair, 🔴 Uncertain — makes every match easy to triage.

Visual verification

Side-by-side comparison views with matched keypoint visualization let you confirm each identification by eye.

Use Cases

Where the system supports conservation work:

Population monitoring

Estimate population sizes in protected areas by identifying unique individuals from camera trap surveys, and track how populations change over time without physical capture.

Movement ecology

Match sightings at different camera trap locations to understand how individuals move across landscapes, mapping territory boundaries and corridor usage.