Wild Me develops open-source platforms for identifying and tracking wildlife, combining the strengths of AI and citizen scientists to fight extinction.Learn more about Wildbook
Without action, 38 percent of all species could be extinct by 2100. Many animal species are especially vulnerable due to poaching, habitat loss, and climate change. Fighting extinction requires a tremendous amount of data, including population size, location, and migration patterns. Gathering this data manually is time-consuming and expensive, making citizen engagement critical to data collection efforts.
Wild Me used computer vision and deep learning algorithms to create a platform called Wildbook, which scans millions of crowdsourced wildlife images at scale. Wildbook can identify the species as well as the individual animal, and the public can follow the movements of their favorite animals. The aggregated data is used by scientists to help inform conservation decisions. Microsoft is supporting their efforts by hosting Wildbook on Azure and making Wild Me’s open source algorithms available as APIs.
Fighting extinction with citizen science
Wild Me is using AI technology and advanced cloud software to identify animal species that are on the verge of extinction. Wild Me uses computer vision and deep learning algorithms to power Wildbook, a platform that scans thousands of images to identify species and individual animals.
How Wild Me works
An animal with unique patterns is photographed by a scientist or volunteer. Images are added to the cloud by users or from social media scans. Computer vision models use pattern recognition to identify the species and individual animal. People can then track their favorite animals on Wildbook. Aggregated data helps scientists monitor populations, animal interactions, and individual movements.
How Wild Me uses Azure
Wild Me uses the following Azure services:
- Translator Text API to translate multi-lingual YouTube videos into English, and to create Wildbooks for global audiences.
- Computer Vision API for optical character recognition, to extract text from YouTube videos, and use it for date/location prediction with machine learning.
- Virtual machines to power Wildbook; Wild Me runs all Wildbooks on high-performing Linux VMs
- Data Science Virtual Machines to create natural language processing (NLP) predictors for our YouTube reviewing intelligent agent.
- Application Insights to monitor Wild Me servers and services for outages.
- Container Registry to store Wildbook containers.
- Azure Backup to back up virtual machines and protect critical wildlife data.
- DevOps for operation monitoring during development.
These components are also available as an architecture diagram.
Wild Me on GitHub
All of Wildbook’s code is open-source and available on GitHub.