Open AccessPosted Content
Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning
Mohammad Sadegh Norouzzadeh,Anh Nguyen,Margaret Kosmala,Alexandra Swanson,Meredith S. Palmer,Craig Packer,Jeff Clune +6 more
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The ability to automatically, accurately, and inexpensively collect such data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, and animal behavior into “big data” sciences is investigated.Abstract:
Having accurate, detailed, and up-to-date information about the location and behavior of animals in the wild would revolutionize our ability to study and conserve ecosystems. We investigate the ability to automatically, accurately, and inexpensively collect such data, which could transform many fields of biology, ecology, and zoology into "big data" sciences. Motion sensor "camera traps" enable collecting wildlife pictures inexpensively, unobtrusively, and frequently. However, extracting information from these pictures remains an expensive, time-consuming, manual task. We demonstrate that such information can be automatically extracted by deep learning, a cutting-edge type of artificial intelligence. We train deep convolutional neural networks to identify, count, and describe the behaviors of 48 species in the 3.2-million-image Snapshot Serengeti dataset. Our deep neural networks automatically identify animals with over 93.8% accuracy, and we expect that number to improve rapidly in years to come. More importantly, if our system classifies only images it is confident about, our system can automate animal identification for 99.3% of the data while still performing at the same 96.6% accuracy as that of crowdsourced teams of human volunteers, saving more than 8.4 years (at 40 hours per week) of human labeling effort (i.e. over 17,000 hours) on this 3.2-million-image dataset. Those efficiency gains immediately highlight the importance of using deep neural networks to automate data extraction from camera-trap images. Our results suggest that this technology could enable the inexpensive, unobtrusive, high-volume, and even real-time collection of a wealth of information about vast numbers of animals in the wild.read more
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Journal ArticleDOI
The role of artificial intelligence in achieving the Sustainable Development Goals
Ricardo Vinuesa,Hossein Azizpour,Iolanda Leite,Madeline Balaam,Virginia Dignum,Sami Domisch,Anna Felländer,Simone D. Langhans,Max Tegmark,Francesco Fuso Nerini +9 more
TL;DR: Using a consensus-based expert elicitation process, it is found that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets.
Posted Content
Tackling Climate Change with Machine Learning
David Rolnick,Priya L. Donti,Lynn H. Kaack,K. Kochanski,Alexandre Lacoste,Kris Sankaran,Andrew S. Ross,Nikola Milojevic-Dupont,Natasha Jaques,Anna Waldman-Brown,Alexandra Luccioni,Tegan Maharaj,Evan D. Sherwin,S. Karthik Mukkavilli,Konrad P. Kording,Carla P. Gomes,Andrew Y. Ng,Demis Hassabis,John Platt,Felix Creutzig,Jennifer Chayes,Yoshua Bengio +21 more
TL;DR: From smart grids to disaster management, high impact problems where existing gaps can be filled by ML are identified, in collaboration with other fields, to join the global effort against climate change.
Journal ArticleDOI
Convolutional neural network: a review of models, methodologies and applications to object detection
TL;DR: This paper mainly focus on the application of deep learning architectures to three major applications, namely (i) wild animal detection, (ii) small arm detection and (iii) human being detection.
Journal ArticleDOI
Applications for Deep Learning in Ecology
TL;DR: It is argued that at a time when automatic monitoring of populations and ecosystems generates a vast amount of data that cannot be effectively processed by humans anymore, deep learning could become a powerful reference tool for ecologists.
Book ChapterDOI
Recognition in Terra Incognita
TL;DR: The CaltechCameraTraps dataset as mentioned in this paper is designed to measure recognition generalization to novel environments, where cameras are fixed at one location, hence the background changes little across images; capture is triggered automatically, hence there is no human bias.
References
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
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