Approaches to Improving the Pre-Excavation Detection of Inhumations

Authors: Green, Cheetham, P. and Darvill, T.

Editors: Jennings, B., Gaffney, C., Sparrow, T. and Gaffney, S.

Conference: AP2017 - 12th International Conference of Archaeological Prospection

Dates: 12-16 September 2017

Journal: 12th International Conference of Archaeological Prospection

Issue: 12

Pages: 90-91

Publisher: Archaeopress Archaeology

Place of Publication: England

ISBN: 9781784916770

Abstract:

As large scale landscape surveys continue to increase in commercial and research archaeogeophysics, there is still a markedly low ability to geophysically detect and interpret archaeological and forensic inhumations in some instances. The aim of this ongoing research project is to improve data acquisition by implementing an interactive ad hoc workflow model for determining appropriate methodologies for ground-penetrating radar (GPR) surveys, to improve data processing speed, and reduce observer error.

Can the confidence of manual interpretations of GPR data be improved by adapting machine learning libraries for automatic object extraction and classification to GPR data based on a training dataset comprised of ground-truthed real GPR data and simulated GPR data?

http://eprints.bournemouth.ac.uk/29725/

Source: Manual

Approaches to Improving the Pre-Excavation Detection of Inhumations

Authors: Green, A., Cheetham, P. and Darvill, T.

Editors: Jennings, B., Gaffney, C., Sparrow, T. and Gaffney, S.

Conference: ICAP 2017: 12th International Conference on Archaeological Prospection

Pages: 90-91

Publisher: Archaeopress Archaeology

Abstract:

As large scale landscape surveys continue to increase in commercial and research archaeogeophysics, there is still a markedly low ability to geophysically detect and interpret archaeological and forensic inhumations in some instances. The aim of this ongoing research project is to improve data acquisition by implementing an interactive ad hoc workflow model for determining appropriate methodologies for ground-penetrating radar (GPR) surveys, to improve data processing speed, and reduce observer error. Can the confidence of manual interpretations of GPR data be improved by adapting machine learning libraries for automatic object extraction and classification to GPR data based on a training dataset comprised of ground-truthed real GPR data and simulated GPR data?

http://eprints.bournemouth.ac.uk/29725/

http://www.archprospection.org/archpros17

Source: BURO EPrints