Persistent Link:
http://hdl.handle.net/10150/194125
Title:
Digital Trails
Author:
Morris, Scott Howard
Issue Date:
2010
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
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.
Type:
text; Electronic Dissertation
Keywords:
Computer Vision; GPS; Libraries; Mapping; Recreation Modeling; Trails
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Computer Science; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Barnard, Kobus
Committee Chair:
Barnard, Kobus

Full metadata record

DC FieldValue Language
dc.language.isoENen_US
dc.titleDigital Trailsen_US
dc.creatorMorris, Scott Howarden_US
dc.contributor.authorMorris, Scott Howarden_US
dc.date.issued2010en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractMay 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.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectComputer Visionen_US
dc.subjectGPSen_US
dc.subjectLibrariesen_US
dc.subjectMappingen_US
dc.subjectRecreation Modelingen_US
dc.subjectTrailsen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorBarnard, Kobusen_US
dc.contributor.chairBarnard, Kobusen_US
dc.contributor.committeememberGimblett, Randyen_US
dc.contributor.committeememberMoon, Bongkien_US
dc.contributor.committeememberGniady, Chrisen_US
dc.identifier.proquest10909en_US
dc.identifier.oclc659754801en_US
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