An image dataset of fusulinid foraminifera generated with the aid of deep learning

Author:

Huang Hanhui1ORCID,Shi Yukun12,Chen Qin1,Xu Huiqing1,Song Sicong1,Shi Yujie1,Shen Furao34,Fan Junxuan12

Affiliation:

1. School of Earth Sciences and Engineering and Frontiers Science Center for Critical Earth Material Cycling Nanjing University Nanjing China

2. State Key Laboratory for Mineral Deposits Research Nanjing China

3. School of Artificial Intelligence Nanjing University Nanjing China

4. State Key Laboratory for Novel Software Technolog Nanjing China

Abstract

AbstractFusulinid foraminifera are among the most common microfossils of the Late Palaeozoic and act as key fossils for stratigraphic correlation, paleogeographic and paleoenvironmental indication, and evolutionary studies of marine life. Accurate and efficient identification forms the basis of such research involving fusulinids but is limited by the lack of digitized image datasets. This article presents the first large image dataset of fusulinids containing 2,400 images of individual samples subjected to 16 genera of all six fusulinid families and labelled to species level. These images were collected from the literature and our unpublished samples through an automatic segmentation procedure implementing BlendMask, a deep learning model. The dataset shows promise for the efficient accumulation of fossil images through automated procedures and will facilitate taxonomists in future morphologic and systematic studies.

Publisher

Wiley

Subject

General Earth and Planetary Sciences

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