Events & News
Colloquium
| Category | Lecture |
| Date | Thursday, April 15, 2010 |
| Time | 10:00 am |
| Location | GS 942 |
| Details | Committee members: Kobus Barnard - Chair Bongki Moon Chris Gniady Randy Gimblett |
| Speaker | Scott Morris |
| Title | PhD Final Defense |
| Affiliation | Computer Science Department |
Digital Trails
Abstract: May your trails be crooked, winding, lonesome, dangerous, leading to the most amazing view. May your mountains rise into and above the clouds.
-- Edward Abbey
The digital representation of trails is a relatively new concept. Only in
the last decade, with increasing adoption and accuracy of GPS technology,
have large quantities of reliable data become a reality. However, the
development of algorithms specific to processing digital trails has not
had much attention. This dissertation presents a set of methods for
collecting, improving and processing digital trails, laying the ground
work for the science of trails.
We first present a solution to the GPS-network problem, which determines
the salient trails and structure of a trail network from a set of GPS
tracklogs. This method has received significant attention from the
industry and online GPS sharing sites, since it provides the basis for
forming a digital library of trails from user submitted GPS tracks.
A set of tracks through a GPS trail network further presents the
opportunity to model and understand trail user behavior. Trail user models
are useful to land managers faced with difficult management decisions. We
present the K-history model, a probabilistic method for understanding and
simulating trail user decisions based on GPS data. We use the K-history
model to evaluate current simulation techniques and show how optimizing
the number of historical decisions can lead to better predictive power.
With collections of GPS trail data we can begin to learn what trails look
like in aerial images. We present a statistical learning approach for
automatically extracting trail data from aerial imagery, using GPS data to
train our model. While the problem of recognizing relatively straight and
well defined roads has been well studied in the literature, the more
difficult problem of extracting trails has received no attention. We
extensively test our method on a 2,500 mile trail, showing promise for
obtaining digital trail data without the use of GPS.
These methods present further possibilities for the study of trails and
trail user behavior, resulting in increased opportunity for the outdoors
lover, and more informed management of our natural areas.