ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume VIII-M-1-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VIII-M-1-2021, 9–16, 2021
https://doi.org/10.5194/isprs-annals-VIII-M-1-2021-9-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VIII-M-1-2021, 9–16, 2021
https://doi.org/10.5194/isprs-annals-VIII-M-1-2021-9-2021

  27 Aug 2021

27 Aug 2021

AUTOMATIZING DEGRADATION MAPPING OF ANCIENT STELAE BY DUAL-BAND IMAGING AND MACHINE LEARNING-BASED CLASSIFICATION

E. Adamopoulos1, F. Rinaudo2, and D. Adamopoulou3 E. Adamopoulos et al.
  • 1Department of Computer Science, Università di Torino, Turin, Italy
  • 2Department of Architecture and Design, Politecnico di Torino, Turin, Italy
  • 3Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece

Keywords: Heritage, Degradation Documentation, Stelae, Stone Weathering, Mapping, Image Classification, Machine Learning

Abstract. Degradation patterns are the visible consequence of the impacts of environmental factors and biological agents on stone heritage. Accurately documenting them is a key requisite when studying exposed stone antiquities to interpret weathering causes, identify conservation needs, and plan cleaning interventions. However, a significant gap can be identified in practical automatized procedures for mapping patterns on stone antiquities, such as ancient stelae. This work evaluates a workflow that uses visible and near-infrared imaging, combined with machine learning-based digital image segmentation tools, to classify degradation patterns on marble stelae correctly and cost-effectively. For this work, different classification methods are considered. Results are analyzed using error matrixes and reference degradation maps. The application cases include stelae displayed in the courtyard of the Archaeological Museum of Eretria (Euboea, Greece). The proposed methodology aims at being easily adapted to facilitate the conservators’ work.