Finding fossils in new ways: An artificial neural network approach to predicting the location of productive fossil localities

Authors

  • Robert Anemone,

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    • Robert Anemone is Professor of Anthropology at Western Michigan University. Having trained in primate functional morphology and vertebrate paleontology at the University of Washington, he has conducted field and museum research on the anatomy and life history of living and fossil primates, and mammalian evolution in North America, Europe and Africa. He has been leading field crews to the Great Divide Basin of southwestern Wyoming since 1994 in order to collect Paleocene and Eocene primates and other mammals. He is the author of Race and Human Diversity: A Biocultural Approach (Prentice-Hall, 2010).

  • Charles Emerson,

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    • Charles Emerson is Associate Professor of Geography at Western Michigan University. A specialist in the theory and practice of quantitative spatial analysis, he received a Ph.D. in Geography at the University of Iowa in 1996 and has recently been examining grassland degradation in Inner Mongolia. Dr. Emerson is one of the developers of the Image Characterization and Modeling System, a software package that measures the fractal dimensions of remotely sensed images.

  • Glenn Conroy

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    • Glenn Conroy is Professor of Anatomy and Anthropology at Washington University School of Medicine, St. Louis, MO. He is known for his work on nearly every epoch of primate and human fossil history. He is author of Primate Evolution and Reconstructing Human Origins, both published by W. W. Norton.


Abstract

Chance and serendipity have long played a role in the location of productive fossil localities by vertebrate paleontologists and paleoanthropologists. We offer an alternative approach, informed by methods borrowed from the geographic information sciences and using recent advances in computer science, to more efficiently predict where fossil localities might be found. Our model uses an artificial neural network (ANN) that is trained to recognize the spectral characteristics of known productive localities and other land cover classes, such as forest, wetlands, and scrubland, within a study area based on the analysis of remotely sensed (RS) imagery. Using these spectral signatures, the model then classifies other pixels throughout the study area. The results of the neural network classification can be examined and further manipulated within a geographic information systems (GIS) software package. While we have developed and tested this model on fossil mammal localities in deposits of Paleocene and Eocene age in the Great Divide Basin of southwestern Wyoming, a similar analytical approach can be easily applied to fossil-bearing sedimentary deposits of any age in any part of the world. We suggest that new analytical tools and methods of the geographic sciences, including remote sensing and geographic information systems, are poised to greatly enrich paleoanthropological investigations, and that these new methods should be embraced by field workers in the search for, and geospatial analysis of, fossil primates and hominins. © 2011 Wiley-Liss, Inc.

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