Endless Forams: >34,000 Modern Planktonic Foraminiferal Images for Taxonomic Training and Automated Species Recognition Using Convolutional Neural Networks

Allison Y. Hsiang; Anieke Brombacher; Marina C. Rillo; Maryline J. Mleneck-Vautravers; Stephen Conn; Sian Lordsmith; Anna Jentzen; Michael J. Henehan; Brett Metcalfe; Isabel S. Fenton; Bridget S. Wade; Lyndsey Fox; Julie Meilland; Catherine V. Davis; Ulrike Baranowski; Jeroen Groeneveld; Kirsty M. Edgar; Aurore Movellan; Tracy Aze; Harry J. Dowsett; C. Giles Miller; Nelson Rios; Pincelli M. Hull

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Endless Forams: >34,000 Modern Planktonic Foraminiferal Images for Taxonomic Training and Automated Species Recognition Using Convolutional Neural Networks

机译：无尽的福尔萨姆：>使用卷积神经网络的分类学培训和自动化物种识别的34,000个现代浮游对敏感图像

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Planktonic foraminiferal species identification is central to many paleoceanographic studies, from selecting species for geochemical research to elucidating the biotic dynamics of microfossil communities relevant to physical oceanographic processes and interconnected phenomena such as climate change. However, few resources exist to train students in the difficult task of discerning amongst closely related species, resulting in diverging taxonomic schools that differ in species concepts and boundaries. This problem is exacerbated by the limited number of taxonomic experts. Here we document our initial progress toward removing these confounding and/or rate-limiting factors by generating the first extensive image library of modern planktonic foraminifera, providing digital taxonomic training tools and resources, and automating species-level taxonomic identification of planktonic foraminifera via machine learning using convolution neural networks. Experts identified 34,640 images of modern (extant) planktonic foraminifera to the species level. These images are served as species exemplars through the online portal Endless Forams (endlessforams.org) and a taxonomic training portal hosted on the citizen science platform Zooniverse (zooniverse.org/projects/ahsiang/ endless-forams/). A supervised machine learning classifier was then trained with ~27,000 images of these identified planktonic foraminifera. The best-performing model provided the correct species name for an image in the validation set 87.4% of the time and included the correct name in its top three guesses 97.7% of the time. Together, these resources provide a rigorous set of training tools in modern planktonic foraminiferal taxonomy and a means of rapidly generating assemblage data via machine learning in future studies for applications such as paleotemperature reconstruction.

机译：浮游有孔虫物种鉴定是许多古海洋学研究的核心，从选择物种进行地球化学研究，到阐明与物理海洋学过程和相互关联的现象（如气候变化）相关的微体化石群落的生物动力学。然而，几乎没有资源来训练学生识别密切相关物种这一艰巨任务，导致在物种概念和边界上存在差异的分类学流派。由于分类学专家数量有限，这一问题更加严重。在这里，我们通过生成第一个广泛的现代浮游有孔虫图像库，提供数字分类学培训工具和资源，并通过使用卷积神经网络的机器学习实现浮游有孔虫物种级分类识别的自动化，记录了我们在消除这些混杂和/或速率限制因素方面的初步进展。专家鉴定了34640张现代（现存）浮游有孔虫的物种级图像。这些图像通过在线门户网站endlessforams（endlessforams.org）和公民科学平台Zooniverse（Zooniverse.org/projects/ahsiang/endlessforams/）上托管的分类学培训门户网站作为物种样本。然后，用这些已识别的浮游有孔虫的约27000张图像训练有监督的机器学习分类器。表现最好的模型在验证集87.4%的时间里为图像提供了正确的物种名称，在前三个猜测中97.7%的时间里包含了正确的名称。总之，这些资源为现代浮游有孔虫分类提供了一套严格的训练工具，并在未来的研究中通过机器学习快速生成组合数据，以用于古温度重建等应用。

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来源
《Paleoceanography》 |2019年第7期|共21页
作者
Allison Y. Hsiang; Anieke Brombacher; Marina C. Rillo; Maryline J. Mleneck-Vautravers; Stephen Conn; Sian Lordsmith; Anna Jentzen; Michael J. Henehan; Brett Metcalfe; Isabel S. Fenton; Bridget S. Wade; Lyndsey Fox; Julie Meilland; Catherine V. Davis; Ulrike Baranowski; Jeroen Groeneveld; Kirsty M. Edgar; Aurore Movellan; Tracy Aze; Harry J. Dowsett; C. Giles Miller; Nelson Rios; Pincelli M. Hull;
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作者单位

Department of Bioinformatics and Genetics Swedish Museum of Natural History Stockholm Sweden;

School of Ocean and Earth Science National Oceanography Centre Southampton University of Southampton Southampton UK;

School of Ocean and Earth Science National Oceanography Centre Southampton University of Southampton Southampton UK;

Godwin Laboratory for Paleoclimate Research Department of Earth Sciences University of Cambridge Cambridge UK;

School of Earth and Ocean Sciences Cardiff University Cardiff UK 6Department of Climate Geochemistry Max Planck Institute for Chemistry Mainz Germany;

School of Earth and Ocean Sciences Cardiff University Cardiff UK;

Department of Climate Geochemistry Max Planck Institute for Chemistry Mainz Germany;

GFZ German Research Centre for Geosciences Potsdam Germany;

Laboratoire des Sciences du Climat et de l'Environnement LSCE/IPSL CEA-CNRS-UVSQ Université Paris-Saclay France;

Department of Life Sciences Natural History Museum London UK;

Department of Earth Sciences University College London London UK;

Department of Earth Sciences Natural History Museum London UK;

MARUM Universit?t Bremen Leobener Stra?e 8 Bremen Germany;

Department of Earth and Planetary Sciences University of California Davis CA USA;

School of Geography Earth and Environmental Sciences University of Birmingham Birmingham UK;

Alfred Wegener Institute Helmholtz Center for Polar and Marine Research Bremerhaven Germany;

School of Geography Earth and Environmental Sciences University of Birmingham Birmingham UK;

School of Geography Earth and Environmental Sciences University of Birmingham Birmingham UK;

School of Earth and Environment University of Leeds Leeds UK;

Florence Bascom Geoscience Center U.S. Geological Survey Reston VA USA;

Department of Earth Sciences Natural History Museum London UK;

Biodiversity Informatics and Data Science Peabody Museum of Natural History Yale University New Haven CT USA;

Department of Geology and Geophysics Yale University New Haven CT USA;

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原文格式 PDF
正文语种 eng
中图分类海洋学;
关键词
Endless Forams: gt; 34; 000 Modern; Planktonic Foraminiferal; Images for;

机译：无尽的福特鸟：＆gt;34;000现代;浮游植物的forminiferal;图像;

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Endless Forams: >34,000 Modern Planktonic Foraminiferal Images for Taxonomic Training and Automated Species Recognition Using Convolutional Neural Networks

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