Accelerating Biodiversity Surveys with AI

Accelerating Biodiversity Surveys with AI


Biodiversity is declining across the globe at a catastrophic rate, as threats from human settlement expansion, illegal wildlife killing, and climate change place enormous pressure on wildlife populations. Conservation biologists are faced with the daunting – but urgent – task of surveying wildlife populations and making policy recommendations to governments and industry. What species need legal protection from hunting? A road needs to connect two cities; which route will have the least detrimental impact on wildlife habitat? Where will it be most effective to build underpasses as wildlife migration corridors? Where should we deploy anti-poaching resources? Informed policy decisions on questions like these start with data: just as your doctor can only prescribe treatment after running trusted diagnostic tests, policy-makers and protected area managers can only act to protect biodiversity if robust, up-to-date data is available when they need it.

Data, in this case, is wildlife population estimates, for which remote sensing has become the most powerful tool in the conservation toolbox. But currently, collecting data about how animals use their habitats – or even how many animals live in a given area – is dependent on tremendous amounts of manual data annotation: it often takes years for a small NGO to annotate millions of images or audio recordings for a single project. This bottleneck precludes real-time applications, and often delays critical answers to conservation questions so long that by the time they’re available, they’re no longer relevant. Machine learning is poised to break this annotation logjam, and to greatly accelerate conservation decision-making.

We apply machine learning tools to a variety of image sources – including motion-triggered camera traps, aerial cameras, and microphones – to accelerate ecologists’ workflows. Our team spans Microsoft Research, AI for Earth, and the AI for Good Research Lab.





Publications and publication-like things

Blogs and blog-like things

Other work at Microsoft

Whoever “we” are (i.e., the “we” who maintain this page), “we” are not the only ones at Microsoft working in this area. Here are some other great projects that our Microsoft colleagues have worked on in the biodiversity space:

Literature reviews

Machine learning doing serious important things

funny species classification picture