If you liked what you saw, learn more about our project and see its full
potential
Zoology is a fun app that can saves life
Join us on our journey!
You want to have a better understanding of the identity of the
project, as well as, our motivation and the goals we want to
reach?
You can find our
Research Question, Context, Target Audience, Features and User
Flow below.
Learn more in depth about our project check this
Zoology is a project that started thanks to the passion and
dedication of five students toward wild animals protection.
This idea came from our willingness to improve wild animals
protection in a way that can be accessible and enjoyable for
people like us and also to attract a broader audience to defend
this cause.
This is how the playful and educational app Zoology was born.
We believe there is a serious lack of education and awareness for endangered wildlife. During our early stages of the project, we discovered a few applications that share the same purpose as us. Here are a few:
WildScan
WildLife Sentinel
WWF
WildScan places critical information on endangered species at the fingertips of those who need it most and provides a tool to report wildlife crime. WildScan contains a comprehensive species library with more than 250 endangered animals commonly smuggled into and throughout Southeast Asia, a global hotspot for wildlife trafficking.
Wildlife Sentinel is an app to help staff at airports, airlines,
and other aviation companies report suspected wildlife trafficking
and corruption. Released by Crime Stoppers International, the app
is already being used by aviation personnel with many reports
providing indicators of wildlife crime.
WWF Together takes users closer than they ever thought to
magnificent and endangered animals, allowing you to learn about
their lives and the work that animal defenders perform for them.
The previously mentioned applications focus either on the identification of species or reporting of suspicious activities. Yet, we believe an incentive to engage users is missing
Zoology incorporates the social aspect and animates users to share their collected photos. The sharing of endangered species will even spread more awareness.
When you take a certain amount of pictures, you'll get rewards! It will incentivize people to take as many pictures as possible which will only strenghten our database.
After having the general idea and concept about the project, we
had to get this more concrete and we therefore, designed a first
wireframe of how the Zoology app will be design. After a first
draft we focused on our main feature which allows to take a
picture/video and using our AI technology analyzes and recognizes
the animal
This also allowed us to have an overview of the app's functions,
without taking into account the design or any visual aspect.
Find the link to our wireframe HERE
Once the wireframe was built, the next step in the realization of the
project was to get the improved draft version of the Zoology app,
using colors, fonts, photos to make it more real. To do so, we used
the Figma platform to design the different mockups needed for the
project. These mockups are demonstrators of our concepts that allowed
us to easily get end-users feedback.The final mock-up version will
give you a better understanding of what our application will look like
and how to use it.
After the first mockup 'Beta Version' has been designed with Figma, it was important to improve it, so we decided to test it among potential users who fit our target audience. We conducted two test sessions with two different users (one male and one female) for each session. Thanks to the testers' feedbacks we were able to make our mockup more intuitive/clearer for users and allowed us to add new useful features such as the ability to report suspicious poaching activities directly to local authorities. You will find the final version in the 'Mock Up' section above
Find the link to the user tests HERE
We're looking for engaged developers and machine learning specialists, who share the same vision and can help us build the algorithm. Computer vision & image recognition make up the core part of this supervised machine learning technique. The algorithm should use images of animals and specific body parts. We aim to train the algorithm on any species using a restricted dataset within four months.
In the future we will continue to collaborate with more (non-)governmental organisations , as the fight against illegal wildlife trafficking requires collective effort. The main language of our application will be English but we intend to release it in Spanish, French, Chinese and Arabic too. We're always welcoming your feedback to continuously improve and save more species!
