Horn, C., Pitman, D. and Potter, R., 2019. An evaluation of the visualisation and interpretive potential of applying GIS data processing techniques to 3D rock art data. Journal of Archaeological Science: Reports, 27 (October), 101971.
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DOI: 10.1016/j.jasrep.2019.101971
Abstract
Rock art provides a tangible visual link to past communities and has significant value in building our under-standing of prehistoric societies. Its recording and interpretation has long provided a window to intangibleaspects of society, such as belief systems and folk narratives. Petroglyphic rock art has traditionally been re-corded through simple rubbing, or frottage, and the majority of interpretations and narratives to date have beenbased on this work. Recently, three-dimensional capture techniques have become readily available and theyreplace traditional approaches to rock art recording. These techniques are valuable, but the data-heavy outputslack the interpretive clarity of traditional methods. This paper explores these issues through a novel approachthat employs topographic landscape analysis techniques, initially developed for LiDAR processing, to produceclear images that have the precision and dimensional accuracy of 3D captured data, but the visual clarity oftraditional methods. Specifically, this paper outlines an approach based on local relief modelling (a techniquethat highlights subtle topographic features) and explores its efficacy through case studies of Bronze AgeScandinavian petroglyphs. This method was developed to aid the analysis of 3D models and to improve vi-sualising the results based on such investigations. This work offers a significant impact on rock art studies as itfacilitates the identification of previously unidentified motifs, and allows a clearer sense of petroglyphic worldviews. The technique can be applied to models of other archaeological surface
Item Type: | Article |
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ISSN: | 2352-409X |
Uncontrolled Keywords: | Visualisation; 3D data; GISRock art; Digital elevation models; Local relief modelling |
Group: | Faculty of Science & Technology |
ID Code: | 32702 |
Deposited By: | Symplectic RT2 |
Deposited On: | 03 Sep 2019 11:08 |
Last Modified: | 14 Mar 2022 14:17 |
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