3 billion microfossil puzzle can be solved with new AI deep learning model

Analyzing microfossils has always been an uphill task for several reasons. However, studying them is crucial because they help map subsurface structures and provide insights into past geological periods.

Notably, applying computer vision to microfossil analysis presents significant challenges. The sheer volume of data, with an estimated 3 billion individual fossils to analyze, can be overwhelming. Additionally, the extreme scarcity of labeled data for training machine learning models further complicates the task.Artificial Intelligence in Geosciences

Moreover, images often contain redundant information stored in pixels surrounding the object of interest. In all such cases,Deep learning methods excel at modeling complex relationships within data. To achieve accurate results, convolutional neural networks are designed to extract meaningful information from images.

“Today, both CNNs and ViTs are commonly used for image classification, and there is no clear winner or best architecture for this task Hence, it is standard to test and compare both architectures, and that is what we do here.”The study’s findings demonstrate that the most promising approach for further research involves obtaining self-supervised training. The researchers emphasized that AI significantly aids in the automatic detection and recognition of fossils.

Source: Education Headlines (educationheadlines.net)

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