FASTCAT-Cloud: upload and analyze all of your nature videos and pictures to the FASTCAT-Cloud website: receive only information on relevant images and recordings of wildlife activity and quickly identify the species names thanks to Artificial Intelligence.

Do you want to get updates on the latest news, activities and progress of Cos4Bio/Env?

Service description

FASTCAT (Flexible Ai System for CAmera Traps)-Cloud is an open website service able to: 

(1) Automatically filter out most unwanted pictures and video streams, keeping images of animals. Therefore, you will save time in removing empty recordings or photos.

(2) Integrate a machine learning technology (AI – Artificial Intelligence) to automatically identify species, which means that you will see the suggested species names for each image.

(3) Provide you with counts (number of species recorded or photographed), i.e., how many different species have been sighted this week, or how many times you have photographed a fox in last 30 days.

(4) Eventually, this website service will connect with biodiversity citizen observatories. So, if you are a citizen scientist that uses a camera trap, you will be able to easily upload the picture to some platforms such as iSpot, Artportalen or Natusfera. The idea is to create a Graphical User Interface (GUI) that allows you to view the proposed species name. If you accept the suggested classification from the system, the picture is uploaded to any of the chosen biodiversity citizen observatories (iSpot, Artportalen, Natusfera, etc.) with the proposed identification name. 


Development & functioning

The website service integrates an artificial intelligent model to decide which frames, both from images or videos, contain an animal. This means that it will remove most images that are not useful (e.g., no animals present, near duplicates, false positives due to wind movement, etc.). The camera trap uses bespoke Artificial Intelligence to automatically identify species and to count the number of species, both from videos and pictures. 

  • Drastically cut the data analysed, archived or transmitted from any camera trap. This will be achieved by:
  • Removing most images that are not useful (i.e., no animals present, near duplicates, false positives due to wind movement, etc.).
  • Provide access to robust state-of-the-art processing methods and algorithms: recent deep learning methods to automatically find animals (detection) and identify the species names. This will be achieved by:
  • Providing automatic bounding boxes for objects of interest, which typically are various animals (the service provides such bounding boxes around animals.).
  • Provide a minimum of automatic analysis of data and make the entire processing of images much easier. This mainly involves three steps:

  • (i) Filtering: remove most frames that are not useful; (ii) provide bounding boxes around animals in images: in this step is where the counting is done; (iii) implement automatic species identification in each bounding box: animal species ID (when robust).
We are working on this service, it will be available soon!

Innovation for the camera trap community

The main problem for camera trap users is that they get hundreds (or thousands) of pictures that they (1) have to filter (some of them just captured the movement of the background plants/trees due to the wind, not because an animal appears) and (2) have to label the images properly. This service will address such problems. 

  • Save time in selecting the images and videos: the website server greatly reduces the amount of unwanted data to store or filter.
  • Easily identify the species names.
  • Design your own observation pipeline around this camera trap, as the camera trap is a general purpose computer.
  • Provides you with counts (number of species recorded or photographed).
  • Share the wildlife images with citizen science projects.

There is no similar service (a) for camera-trap systems (partial solutions exist) and (b) on the cloud.


Camera trap, artificial intelligence, wildlife activity, machine learning.

Test the FASTCAT-Cloud demo:

Related news:


Questions & answers

  • Will the FASTCAT-Cloud website be able to filter out both images and videos?


  • Are the videos and images I upload to the FASTCAT-Cloud website stored?
No, the photos and videos are not stored, just available for downloading, which means that you will receive back the videos or images that contain animals when you upload your pictures/videos to the website.
  • Can I select which species subsets I want to download? For example, if I’m interested only in receiving the images/videos with foxes?
Yes. You can select the specific species images/videos you want, and the system will send you only these.
  • In which formats can I upload photos and videos to the FASTCAT-Cloud website?
In the standard formats: .jpg, .png, .mov, .mp4, etc. So, this shouldn’t be a problem!
  • Can the system detect humans?
Yes, the system can detect humans (as an animal class); and they can be removed or counted as such. However, we do not provide recognition of any other features (gender, etc.), just the label “human.”

Co-design highlights

People involved

Most of the attendees who answered the post-event survey would recommend the co-design session to a friend.

Most of the attendees who answered the post-event survey have learned useful information about how to apply co-design to citizen science.

Want to join the co-design community?

Interact and meet other professionals working in your field

You will be able to network with other professionals and projects.

Help boost the functionalities of citizen observatories

For example, improve species identification with artificial intelligence, data integration from various citizen science platforms, etc.

Be an active part of the open science movement

Cos4Cloud's technical services are open source and are intended to be adapted and improved by the community involved.

Learn about citizen science, technology, and co-design

Also, we will tell you how we have applied co-design feedback in developing the service at the end of the project.