From remote sensing and machine learning to the history of the Silk Road: large scale material identification on wall paintings

Kogou, S., Shahtahmassebi, G. ORCID: 0000-0002-0630-2750, Lucian, A., Liang, H. ORCID: 0000-0001-9496-406X, Shui, B., Zhang, W., Su, B. and van Schaik, S., 2020. From remote sensing and machine learning to the history of the Silk Road: large scale material identification on wall paintings. Scientific Reports, 10: 19312. ISSN 2045-2322

[img]
Preview
Text
1387921_Liang.pdf - Published version

Download (2MB) | Preview

Abstract

Automatic remote reflectance spectral imaging of large painted areas in high resolution, from distances of tens of meters, has made the imaging of entire architectural interior feasible. However, it has significantly increased the volume of data. Here we present a machine learning based method to automatically detect ‘hidden’ writings and map material variations. Clustering of reflectance spectra allowed materials at inaccessible heights to be properly identified by performing non-invasive analysis on regions in the same cluster at accessible heights using a range of complementary spectroscopic techniques. The world heritage site of the Mogao caves, along the ancient Silk Road, consists of 492 richly painted Buddhist cave temples dating from the fourth to fourteenth century. Cave 465 at the northern end of the site is unique in its Indo-Tibetan tantric Buddhist style, and like many other caves, the date of its construction is still under debate. This study demonstrates the powers of an interdisciplinary approach that combines material identification, palaeographic analysis of the revealed Sanskrit writings and archaeological evidence for the dating of the cave temple paintings, narrowing it down to the late twelfth century to thirteenth century.

Item Type: Journal article
Publication Title: Scientific Reports
Creators: Kogou, S., Shahtahmassebi, G., Lucian, A., Liang, H., Shui, B., Zhang, W., Su, B. and van Schaik, S.
Publisher: Springer
Date: December 2020
Volume: 10
ISSN: 2045-2322
Identifiers:
NumberType
10.1038/s41598-020-76457-9DOI
1387921Other
Rights: © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Divisions: Schools > School of Science and Technology
Record created by: Laura Ward
Date Added: 05 Jul 2021 09:22
Last Modified: 30 Jul 2021 14:15
Related URLs:
URI: http://irep.ntu.ac.uk/id/eprint/43305

Actions (login required)

Edit View Edit View

Views

Views per month over past year

Downloads

Downloads per month over past year