Using remote sensing and machine learning to reconstruct paleoenvironmental features in the Koobi Fora Formation

Abstract

Advances in Geographic Information Systems and Remote Sensing technologies have the potential to revolutionize archaeological and paleontological fieldwork. Machine learning models have been effective in identifying conditions ideal for preservation, exposure, and discovery of fossils in a range of geographic contexts. Researchers working in the Koobi Fora Formation of northern Kenya have long inquired about the geographic patterning of extinct fauna and their respective paleoenvironments. This project is the first attempt to use machine learning techniques to capture paleoecological patterns utilizing topographical and spectral variables that may be predictive of the input of aquatic components in the paleoenvironments of the Koobi Fora Formation.Koobi Fora Research and Training Program - NSF Archaeology Program [1624398]FCTPortuguese Foundation for Science and TechnologyEuropean Commission [SFRH/BD/122306/2016]Boise Trust FundREU [1930719]info:eu-repo/semantics/publishedVersio