Advancing paleontology: a survey on deep learning methodologies in fossil image analysis

Abstract

Understanding ancient organisms and their interactions with paleoenvironments through the study of body fossils is a central tenet of paleontology. Advances in digital image capture now allow for efficient and accurate documentation, curation, and interrogation of fossil forms and structures in two and three dimensions, extending from microfossils to larger specimens. Despite these developments, key fossil image processing and analysis tasks, such as segmentation and classification, still require significant user intervention, which can be labor-intensive and subject to human bias. Recent advances in deep learning offer the potential to automate fossil image analysis, improving throughput and limiting operator bias. Despite the emergence of deep learning within paleontology in the last decade, challenges such as the scarcity of diverse, high quality image datasets and the complexity of fossil morphology necessitate further advancement which will be aided by the adoption of concepts from other scientific domains. Here, we comprehensively review state-of-the-art deep learning based methodologies applied to fossil analysis, grouping the studies based on the fossil type and nature of the task. Furthermore, we analyze existing literature to tabulate dataset information, neural network architecture type, and key results, and provide textual summaries. Finally, we discuss novel techniques for fossil data augmentation and fossil image enhancements, which can be combined with advanced neural network architectures, such as diffusion models, generative hybrid networks, transformers, and graph neural networks, to improve body fossil image analysis.