Aram Andreasyan
November 17, 2025

When Wildlife Photos Become More Than Photos

Turning everyday field images into useful conservation data

The idea was simple: what if the photos people take in the wild could help researchers understand animals better? Not just as beautiful shots, but as pieces of information — time, place, behaviour, patterns. The problem was that researchers rarely had access to this amount of material, and photographers rarely had a place to send it. That gap became the starting point of this work.

I knew a standard UX process wouldn’t be enough. The subject was too specific, and I didn’t come from a wildlife research background. So I invited the people who knew the field best — photographers and researchers — and built the project together with them.

Aram Andreasyan

Where the Problem Actually Starts

Researchers often struggle with one simple issue:
they don’t have enough data, and they can’t be everywhere at the same time.

To understand this better, I imagined a small example.
A researcher studying foxes may visit a few locations, take notes, and gather photos. But:

  • he can’t cover all regions,
  • and he can only collect data during the limited days he is in the field.

Meanwhile, photographers across the country are capturing hundreds of images every week — but most of these photos stay in private folders and never become useful data.

This imbalance was the real opportunity.

Why I Turned to Participatory Design

Instead of making assumptions, I wanted to learn directly from the people involved. So I ran a series of simple activities with two groups:

  • wildlife photographers
  • wildlife researchers

My goal was to understand what each group actually needs, how they work, and how photos could naturally become part of a larger data system.

What We Learned Together

1. Time matters more than quantity

In an early activity, researchers compared single photos vs. a series captured over days.
What mattered most wasn’t how many photos they had — it was how long the species was observed. Location and time made the difference.

2. Photographers have far more data than they realize

When I visited photographers and looked at their folders, the scale surprised all of us.
A single trip could bring back 1,000+ images, but only five or six ever reached social media.
Yet inside those folders, there were often 20–30 species recorded in one visit.

This showed one clear insight:
photographers already collect valuable data — they just don’t use it as data.

3. Existing record-keeping is too basic

Most photographers track sightings with notes or spreadsheets. It’s simple, but not accurate.
Researchers need verified information, not checklists.

4. Photographers want to see their journeys clearly

Through a “circles of me” and sketching exercise, photographers showed what they value most:

  • a clean map of their sightings
  • frequency of each species
  • their most active locations

This became the base of the design.

Building the Platform

Using all these insights, I shaped two main dashboards:

For Photographers

A place where they can upload images easily — drag, drop, done.
Most details fill in automatically from EXIF data.
They get:

  • a map of their sightings
  • trip summaries
  • species lists
  • and a personal visual record of their progress

A small leaderboard was added to encourage regular uploads, but kept simple and friendly.

For Researchers

They see:

  • hotspot maps
  • species frequency
  • locations over time
  • and a clearer view of animal presence across regions

Instead of a few field trips, they now get a growing, long-term dataset created naturally by photographers.

Citizen Scientists Join In

Some tasks require many eyes — counting species in images, identifying animals, or tagging patterns.
So the platform also welcomes wildlife enthusiasts who want to help.
Their contributions support researchers and create a wider community around conservation.

What Comes Next

A possible next step is connecting all this with AI — not to replace people, but to learn from them. As more images are uploaded and identified, an AI model could help researchers sort and analyze the data faster.

Final Thoughts

This project showed me that rigid UX processes don’t always fit real-life problems. Sometimes the best results come from sitting down with people, listening, and building the solution with them, not for them. Participatory design made that possible.