Skip to main content

An evaluation of Image-Based Modelling for metrically recording cultural heritage subjects suitably to enable further use in geomatics, geoinformatics, and digital humanities.

Rowley, R., 2021. An evaluation of Image-Based Modelling for metrically recording cultural heritage subjects suitably to enable further use in geomatics, geoinformatics, and digital humanities. Masters Thesis (Masters). Bournemouth University.

Full text available as:

[img]
Preview
PDF
ROWLEY, Richard_M.Res._2021.pdf

5MB

Abstract

Image-Based Modelling (IBM) produces 3D models using Structure-from- Motion (SfM) to extrapolate geometry from sets of photographs in combination with additional spatial data for scaling and orientation. This project examines the extent to which such models of cultural heritage subjects may be created to quantifiable levels of accuracy to enable their further use for scientific study, archival record or wider dissemination and promotion. It also aims to further the potential impact of citizen scientists. Demonstrably accurate datasets may also contribute to the management of at-risk in-situ material in high energy marine or coastal environments, as well as being a viable methodology for preservation by record; so long as the results can be demonstrated to be accurate. To date, much literature has considered the potential applications of such datasets, but much less research has focused on technical standards, quantitative assessments of accuracy, or the development of creator-level evaluation methodologies.

Item Type:Thesis (Masters)
Additional Information:If you feel that this work infringes your copyright please contact the BURO Manager.
Uncontrolled Keywords:Photogrammetry; Image Based Modelling (IBM); Structure-from-Motion (SfM); in-situ Recording of Cultural Heritage; Quantitative Methodologies; Geomatics; Geoinformatics; Microsoft Excel; Direct Survey Method; Digital Humanities; Marine, Coastal and Underwater Archaeology; Citizen Science
Group:Faculty of Science & Technology
ID Code:35419
Deposited By: Symplectic RT2
Deposited On:20 Apr 2021 13:59
Last Modified:14 Mar 2022 14:27

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -