Conservation researchers rely on automated camera traps to provide them with the raw data they need for their work. The images captured help us know when species are in decline or recovery, if they’re being forced to migrate by climate change or predators, and when humans begin interfering in remote parts of the globe.
The data these images provide is vital to understanding and protecting our world. However, obtaining this data is a big challenge. Not only are there hardware challenges facing the use of camera traps – once the images are collected, they’re manually tagged and categorized. This can result in months-long delays in gaining vital insights.
Autofocus uses AI and data science modeling to vastly speed up the labeling process by automatically classifying the images camera traps take.
The application can reduce the amount of work it takes to label photos by 97%, freeing up valuable time and resources for researchers to analyze, evaluate and take action on the results.
Our goal is to help researchers save time and money that they can add back into their conservation efforts.
It works with popular conservation industry technology to create a scalable solution for conservationists and researchers. The interface is simple and easy to use, enabling organizations to quickly get started and gain insights from their data.
It performs auto-classifications of images to detect species and alert researchers and activists.
Our analytics dashboard enables researchers to clearly map the locations of species and determine actionable next steps based on data visualizations of image classifications.
The power of AI