Department of Brain and Cognitive Sciences Department of Earth and Environmental Sciences: Paleomagnetic Research Group MVRL: Multidisciplinary Vision Research Laboratory

An Active Vision Approach to Understanding and Improving Visual Training in the Geosciences


to our research project page

LATEST UPDATES: June 29, 2014

Welcome! This site contains all the public information, imagery, results, publications, and code for our “GeoVis” or “Active Vision” project.
Here, we are studying mobile eye-tracking, natural-scene imagery, geoscience expertise, and the pedagogy of geoscience through imaging technologies.
This is an NSF-sponsored research project under the Co-PIs (Principal Investigators): Dr. Robert A. Jacobs, Dr. John A. Tarduno, and Dr. Jeff B. Pelz, in a collaboration between the University of Rochester (UofR) and the Rochester Institute of Technology (RIT).
Below you will find the abstract from the NSF proposal, and the rest of the information about the project can be found through the navigation links above.
Relevant pages (broken-up by project “stages”) will contain open-source software, results demonstrations, and some of our free-to-use data.
For any questions, comments, or requests, please feel free to contact Tommy P. Keane or Dr. Jeff B. Pelz.

Abstract from the original proposal:

An Active Vision Approach to Understanding and Improving Visual Training in the Geosciences

Field experience is a fundamental part of the training of student geologists, but practical considerations limit the numbers of students who can take part in extensive field programs. Moreover, little is known about how novice geologists acquire the visual skills of experts, raising questions about how best to develop teaching interventions. The 5-year project investigates differences between expert and novice geoscientists in the field and in a virtual semi-immersive display environment. The research team is composed of scientists and educators with expertise in perceptual learning, geology and geophysics, the recording and analyzing of eye movements, and large-field-of-view image capture of natural environments. They hypothesize that there are large differences between the eye-movement sequences of experts and novices, and that novices will show improvement during a field trip. The researchers will study similar groups in a virtual environment, hoping to gain additional insight into learning through comparisons of the data collected in the two environments. Their ultimate goal is to design a virtual semi-immersive environment that replicates the salient aspects of the field learning experience.