Citizen science to observe animals in winter

using photos of their footprints in the snow

Feedback Loop: Taxonomic ID from Photo Recognition, Integrated Species Distribution Modeling, and Citizen Science.

A TETTRIs Third Party Project funded by the European Union.

Try our pilot reporting form! (English or Norwegian)

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Partnering with the public in nature research

About FOOTPRINTs

Our mission is to open up new ways for the public to participate in collecting data on animal populationsthrough the collection of photographs of animal footprints in the snow.


To bring this vision to life, we are building new research collaborations and supporting them with new research methods. We’re developing machine learning models to identify an animal from a photo of its footprints. We’re advancing biodiversity modeling to pinpoint locations where we need more data to understand the conservation status of animal populations. And we’re refining data collection methods to find ways to partner with the public in this work.


Data about the natural world is a foundation of nature restoration, wildlife protection, and science-based nature managementand we believe that there is room for everyone to be a part of this mission.

A participant in a Wild Lab Projects tour group collects photo data for the FOOTPRINTs project.

FOOTPRINTs

Partnering with the public in nature research

We partner with the public to collect data on animal populations in winter. Combining citizen science collection of snow footprint photograph , state-of-the-art AI species identification models, and live data analysis, we provide participants and stakeholders with new raw data and updated species maps.

5 partners

Meet our team with

the links above

22 months

Launched

January 2024

3 study sites

...and

counting!

A feedback loop for research innovation

Building new research tools for biodiversity research

Citizen science

Strengthening connections between communities and local natural areas

Developing rigorous data collection protocols to put data to use for biodiversity research and conservation

Making nature experiences more regenerative, meaningful, and impactful

Machine learning species identification

Building state-of-the-art machine learning models to identify species from a new type of data: footprint photos

Developing novel workflows that will be openly shared with the research community

Creating an app interface for real-time species identification for citizen science participants

Integrated species distribution models

Combining the strengths of many data types to produce up-to-date estimates of animals species' distributions

Validating workflows that will be made openly available for modeling species distributions anywhere in the world

Pinpointing locations for expansion of citizen science data collection


This project has received funding from the European Union’s Horizon Europe Research and Innovation programme within the framework of the TETTRIs Project funded under Grant Agreement Nr. 101081903.

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