Doctoral Dissertation Research: Predicting the location of hominin cave fossil sites with a machine learning approach

Louisiana State University, Baton Rouge LA

Investigators

Abstract

Surveying an area for new fossil sites is a time- and labor-intensive activity. However, machine learning models trained in remotely sensed imagery can be used to predict the location of these sites. The purpose of this research is to build a predictive machine learning model for cave sites suited to the unique environmental and preservation context hominin fossil cave sites. This doctoral dissertation research seeks to (1) determine which environmental and geomorphological variables are most influential in the distribution of caves in this context, and (2) transform the process by which cave fossil sites are discovered by integrating remote sensing and machine learning techniques. This is an interdisciplinary project that supports graduate training and research in STEM and seeks to promote future collaborative projects via online data sharing, conference talks, publications, and public engagement. In this project, remotely sensed high-resolution images of the study area that document surface reflection and elevation, in addition to geologic data, serve as the sources for input predictor variables in machine learning prediction models. The models use these inputs to identify complex patterns between characteristics of Plio-Pleistocene hominin cave sites and locate areas with a similar suite of features for fossil prospecting. This research compares different machine learning algorithms to determine which one is best suited to predict cave sites within this context, conducts in-field ground-truthing assessments of the best performing model, and analyzes model utility outside the study area. This project examines the geomorphological characteristics that indicate subterranean fossil sites at the surface level and seeks out new potential localities for fossil prospecting. This quantitative approach helps reduce the barriers impeding site discovery and aims to increase fossil recovery in paleoanthropology. This project is jointly funded by the Biological Anthropology Program and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.