Description
Monitor animal populations
Linked Case Studies
Case Study
The Zoological Society of London
The Zoological Society of London is preventing poaching with machine vision and machine learning
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US National Oceanic and Atmospheric Administration
The U.S. National Oceanic and Atmospheric Administration acoustically detects humpback whales using a convolutional neural network
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Serengeti Snapshot
Researchers identified and counted wildlife with 96.6% accuracy using image recognition machine learning
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University of Washington
University of Washington researchers built the DeepSqueak neural net program to analyse mouse noises
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Rainforest Connection
Rainforest Connection detects illegal logging over 2,500 sq km of rainforest
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Wildlife Protection Solutions
Wildlife Protection Solutions combats illegal wildlife poaching with deep learning
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Northern Territory Department of Primary Industry and Resources
The Northern Territory Department of Primary Industry and Resources achieves 95% accuracy identifying fish with machine learning
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Harvard University
Researchers at Harvard University and the University of Tübingen track animals’ movements in the lab using deep learning
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Baidu
Baidu builds automated cat shelter for strays using image recognition
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Cornell University
Cornell University researchers increase processing efficiency 300x by developing Adaboost algorithm to automate detection of elephant rumblings
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Universidad de la República de Uruguay
Universidad de la Republica de Uruguay researchers successfully identify varying types of bat species using machine learning to evaluate audio signals
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Verily
Verily aims to reduce infected mosquitos population with computer vision