Vulcan, the Seattle-based organization built by Microsoft co-founder Paul Allen, has a long history of supporting research and initiatives that make a global impact. Now the Vulcan Impact team is continuing its commitment to better protect wild plant and animal species and their habitat by using AI for wildlife conservation.
AI-enabled products that can record and monitor African wildlife come with their share of challenges. In addition to requiring massive amounts of training data, the diversity of the data must account for species, landscape, cultural relevance, and human influence.
Unmanned aerial vehicles (UAVs) have proven to be a viable way to capture large amounts of data, however, these aerial surveys result in countless hours of video footage that can make finding value in the data collected challenging.
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To ensure the highest quality training data, Vulcan partnered with Sama, hiring a dedicated team of data annotators to put bounding boxes around key areas of interest in videos and images, and then pass the data back to Vulcan’s machine learning team to build various ML models.
The Sama team went through a training period aimed at delivering quality annotation at scale and developing subject matter expertise for Vulcan’s specific use case.
To date, Sama has labeled over 600,000 images for Vulcan, having achieved a quality SLA of 95% in support of their efforts to use AI for wildlife conservation. With Sama’s help, Vulcan is able to expedite the processing of data collected from UAVs, without compromising on quality.
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Vulcan’s effort to enhance remote identification of animals has the potential to make a huge impact on wildlife conservation, allowing monitoring to be done over a larger area and faster than can be done on foot, or even in vehicles.
Additionally, by automatically detecting visual anomalies with artificial intelligence, Vulcan hopes to enable rapid-response human-wildlife conflict and loss of habitat, and potentially use this technology to monitor ecosystems or update censuses of animal species. Vulcan’s efforts could also provide even greater situational awareness to rangers in the parks of Africa and other parts of the world, alerting them in advance of pernicious activities, so they’re better prepared to respond.
We had a ton of pictures of cows from Washington, where we are, but cows look different in Africa. Diversity in the dataset has been super challenging.
Once you have a trained team, it pays off because they know what to look for. Some objects just blend into the background in images, so you need a trained eye to spot them.
Sama provides ML Professionals and AI team Leads with an indispensable solution for Computer Vision data labeling.
