Case Studies How Vulcan is Using AI for Wildlife Conservation

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	How Vulcan is Using AI for Wildlife Conservation

How Vulcan is Using AI for Wildlife Conservation

Technology Category	Analytics & Modeling - Machine Learning Analytics & Modeling - Predictive Analytics
Applicable Industries	Agriculture National Security & Defense
Applicable Functions	Field Services Quality Assurance
Use Cases	Remote Asset Management
Services	Data Science Services System Integration
Challenge	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. If processed by humans alone, the work can prove to be mundane when there’s nothing of interest on the screen for hours on end. This is where machine learning proves useful, and the accuracy of the model depends on the accuracy of the data used to train the algorithm. 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. Read More
About Customer	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. 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. Vulcan's efforts are focused on enhancing remote identification of animals, enabling rapid-response to human-wildlife conflict, and monitoring ecosystems. The organization collaborates with various partners to ensure the highest quality of data and technology implementation, aiming to make a significant impact on wildlife conservation globally. Read More
Solution	It was imperative that Vulcan partner with an expert on training data annotation given that any mistakes could lead to inaccuracies in the ML model. 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. 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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Analytics & Modeling - Predictive Analytics

Applicable Industries

Agriculture

National Security & Defense

Applicable Functions

Field Services

Quality Assurance

Use Cases

Remote Asset Management

Services

Data Science Services

System Integration

Challenge

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. If processed by humans alone, the work can prove to be mundane when there’s nothing of interest on the screen for hours on end. This is where machine learning proves useful, and the accuracy of the model depends on the accuracy of the data used to train the algorithm. 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.

About Customer

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. 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. Vulcan's efforts are focused on enhancing remote identification of animals, enabling rapid-response to human-wildlife conflict, and monitoring ecosystems. The organization collaborates with various partners to ensure the highest quality of data and technology implementation, aiming to make a significant impact on wildlife conservation globally.

Solution

It was imperative that Vulcan partner with an expert on training data annotation given that any mistakes could lead to inaccuracies in the ML model. 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. 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.

Impact #1	The Vulcan Impact team’s work to survey wildlife in sub-Saharan Africa is just a small fraction of the work being done for wildlife conservation. On any given day, there are multiple projects in flight, all working toward the ultimate goal to protect endangered species and ensure stable and thriving generations of wild animals.
Impact #2	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.
Impact #3	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.

Impact #1

The Vulcan Impact team’s work to survey wildlife in sub-Saharan Africa is just a small fraction of the work being done for wildlife conservation. On any given day, there are multiple projects in flight, all working toward the ultimate goal to protect endangered species and ensure stable and thriving generations of wild animals.

Impact #2

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.

Impact #3

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.

Benefit #1	Sama has labeled over 600,000 images for Vulcan.
Benefit #2	Achieved a quality SLA of 95% in support of their efforts to use AI for wildlife conservation.

Benefit #1

Sama has labeled over 600,000 images for Vulcan.

Benefit #2

Achieved a quality SLA of 95% in support of their efforts to use AI for wildlife conservation.

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Overview

How Vulcan is Using AI for Wildlife Conservation

Operational Impact

Quantitative Benefit