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

Humanities

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

S. Kogou, G. Shahtahmassebi, et al.

Discover how cutting-edge machine learning techniques can unveil hidden writings and material variations in ancient wall paintings! This groundbreaking research, conducted by Sotiria Kogou, Golnaz Shahtahmassebi, Andrei Lucian, Haida Liang, Biwen Shui, Wenyuan Zhang, Bomin Su, and Sam van Schaik, sheds light on the rich history of Mogao Cave 465, dating its exquisite paintings to the late 12th to 13th centuries.

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Playback language: English
Abstract
This study presents a machine learning-based method for automatically detecting hidden writings and mapping material variations in large-scale wall paintings. Reflectance spectra from wall paintings in Mogao Cave 465, a UNESCO World Heritage site, were analyzed using a Kohonen Self-Organizing Map (SOM) clustering algorithm. This allowed for material identification at inaccessible heights by comparing spectra to accessible areas in the same cluster. Combining material identification, palaeographic analysis of Sanskrit writings, and archaeological evidence, the study narrows down the date of Cave 465 paintings to the late 12th to 13th centuries.
Publisher
Scientific Reports
Published On
Nov 09, 2020
Authors
Sotiria Kogou, Golnaz Shahtahmassebi, Andrei Lucian, Haida Liang, Biwen Shui, Wenyuan Zhang, Bomin Su, Sam van Schaik
Tags
machine learning
wall paintings
Mogao Cave 465
material identification
Kohonen Self-Organizing Map
palaeographic analysis
Sanskrit writings

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