AI Use Case
Monitor animal populations
Detecting, identifying and monitoring animal populations is especially useful when they are in the wild and potentially at risk. Typically it requires a network of static camera traps or acoustic recorders but can also be extended to include mobile sensors.
Risk reduction - Environmental impact,Operational Support - Situational awareness
Serengeti Snapshot~Researchers identified and counted wildlife with 96.6% accuracy using image recognition machine learning,Cornell University~Cornell University researchers increase processing efficiency 300x by developing Adaboost algorithm to automate detection of elephant rumblings,Harvard University~Researchers at Harvard University and the University of Tübingen track animals’ movements in the lab using deep learning,Verily~Verily aims to reduce infected mosquitos population with computer vision ,The Zoological Society of London~The Zoological Society of London is preventing poaching with machine vision and machine learning,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,Wildlife Protection Solutions~Wildlife Protection Solutions combats illegal wildlife poaching with deep learning,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,US National Oceanic and Atmospheric Administration ~The U.S. National Oceanic and Atmospheric Administration acoustically detects humpback whales using a convolutional neural network,University of Washington~University of Washington researchers built the DeepSqueak neural net program to analyse mouse noises,Baidu~Baidu builds automated cat shelter for strays using image recognition,"Rainforest Connection~Rainforest Connection detects illegal logging over 2,500 sq km of rainforest"
Public And Social Sector
Model - Supervised Learning,Model - Semi-supervised Learning,Machine Learning (ML),Image Processing - Feature Extraction