World Wildlife Fund for Nature Indonesia (WWF-Indonesia) has selected Amazon Web Services (AWS) as a preferred cloud provider to help save “critically endangered” orangutans in Indonesia.
Terms of the partnership will see the non-profit organisation roll out machine learning capabilities via the cloud to “better understand” the size and health of orangutan populations in their native habitat. The move aims to allow the surveying of more territories with fewer resources, in an attempt to reduce operating expenses and channel more conservation funding into protecting the "biodiversity of Indonesia".
Leveraging AWS, WWF-Indonesia now automatically gathers images from mobile phones and motion-activated cameras at its basecamp, before uploading to Amazon Simple Storage Service (Amazon S3) to be analysed.
Using technologies including Amazon SageMaker - a fully-managed machine learning service - WWF Indonesia has reduced analysis time from up to three days to less than ten minutes. The organisation has also increased the accuracy and specificity of its data, which includes measurements such as gender ratio and age, in addition to assessing the viability of populations and quickly identifying whether individuals are pregnant, ill, or suffering from injuries which require immediate treatment.
Furthermore, the shift to AWS has enabled WWF-Indonesia to reduce reliance on a limited pool of conservationist experts, while engaging the accuracy and breadth of data specific to orangutan populations.
“As a non-profit, we are always looking for ways to work smarter and apply our resources more effectively to better carry out our conservation mission,” said Aria Nagasastra, finance and technology director of WWF-Indonesia.
“Using AWS services like Amazon SageMaker and Amazon S3, we are starting to make a tool accessible for the field surveyors, even with limited expertise and capacity, to identify wildlife in its natural habitat with a high degree of accuracy."
According to Nagasastra, the deployment will help biologists and conservationists to “effectively and cost-efficiently” monitor wildlife behaviour over an extended period of time, which in turn enables the organisation to allocate appropriate resources to scale up monitoring efforts and conservation actions.
“The collaboration between WWF-Indonesia and AWS on innovative new technology solutions can lead to the opportunity to elevate the biodiversity conservation practices in Indonesia to the next level,” Nagasastra added.
Over the years, human activities including "poaching, destruction of habitat, and the illegal pet trade" have caused severe declines in the orangutan population, which is comprised of three species of great apes native to Indonesia and Malaysia.
According to WWF, Bornean orangutan populations have declined by more than 50 per cent over the past 60 years, with the species’ habitat reduced by at least 55 per cent over the past 20 years.
Since 2005, WWF-Indonesia has assessed the health of orangutan populations and conserved their 568,700-hectare habitat in Sebangau National Park in Central Kalimantan, Indonesia.
Previously, the assessment required experts and local community volunteers going into the field daily to find orangutans, photograph them, download images to local computers at the basecamp, and transport the data back to the city for analysis by a WWF expert. This manual process took WWF-Indonesia experts up to three days to analyse each batch of thousands of photos, a process that could be error-prone due to the volume of data.
“Empowering non-profit organisations with cloud technology to make the world a better place is a priority for AWS in the public sector,” said Peter Moore, regional managing director of Worldwide Public Sector across Asia Pacific and Japan at AWS.
"We are very pleased to help WWF-Indonesia accelerate their mission to conserve the orangutan population, and we are committed to helping them to innovate further on AWS to protect other endangered species around the world."
Looking ahead, WWF-Indonesia plans to explore the use of additional machine learning services, such as Amazon Rekognition, an image and video analysis service, to further improve the speed and accuracy of its population identification and tracking efforts.