Accelerating Image-Based Biodiversity Surveys

Accelerating Image-Based Biodiversity Surveys


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 imaging 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 image annotation: it often takes years for a small NGO to annotate millions of images 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. Computer vision 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 microscopy – to accelerate ecologists’ workflows. Our team spans Microsoft Research and AI for Earth.


Accelerating the processing of images from motion-triggered camera traps

Detecting seals in aerial imagery (in collaboration with NOAA Fisheries)

Species classification from handheld photos

Active learning for wildlife detection in aerial images


In collaboration with external partners, we maintain an extensive repository of labeled conservation images, the Labeled Information Library of Alexandria: Biology and Conservation, aka “LILA”, pronounced “lie-la”.


Species classification from handheld photos


This work is the basis for the following APIs:

Publications and publication-like things

Beery S, Liu Y, Morris D, Piavis J, Kapoor A, Meister M, Perona P. Synthetic examples improve generalization for rare classes. Winter Conference in Applications of Computer Vision (WACV), Aspen, CO, 2020.

Norouzzadeh M, Morris D, Beery S, Joshi N, Jojic N, Clune J. A deep active learning system for species identification and counting in camera trap images. arXiv preprint arXiv:1910.09716. 2019 Oct 22.

Beery S, Morris D, Yang S. Efficient Pipeline for Camera Trap Image Review. KDD Workshop Data Mining and AI for Conservation (DMAIC), Anchorage, AK, August 2019.

Kaeser-Chen C, Birch T, Chou K, Gadot T, Adam H, Belongie S, Robertson T, Fegraus E, Morris D. Towards Ethical Deployment of AI for Conservation Systems. KDD Workshop Data Mining and AI for Conservation (DMAIC), Anchorage, AK, August 2019.

Beery S, Morris D, Yang S, Simon M, Norouzzadeh M, Joshi N. Efficient Pipeline for Automating Species ID in new Camera Trap Projects. Biodiversity Information Science and Standards. 2019 Jun 19;3:e37222.

Blogs and blog-like things

Developing Artificial Intelligence to Find Ice Seals and Polar Bears from the Sky”. NOAA Fisheries News.

Accelerating biodiversity surveys with Azure Machine Learning”. Azure and AI for Earth on Medium.

Other stuff

We maintain a survey of everything we know about machine learning and camera traps.

…and also a survey of everything we know about machine learning and aerial wildlife surveys.

Machine learning doing serious important things

funny species classification picture