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Article

Detection of Archaeological Surface Ceramics Using Deep Learning Image-Based Methods and Very High-Resolution UAV Imageries

1
Earth Observation Cultural Heritage Research Lab, Department of Civil Engineering and Geomatics, Faculty of Engineering and Technology, Cyprus University of Technology, Limassol 3036, Cyprus
2
Eratosthenes Centre of Excellence, Limassol 3036, Cyprus
3
Archaeological Research Unit, University of Cyprus, Nicosia 1678, Cyprus
4
Department of Classics, School of Histories and Humanities, Trinity College Dublin, The University of Dublin, D02 PN40 Dublin, Ireland
*
Author to whom correspondence should be addressed.
Academic Editors: Christine Fürst, Hossein Azadi, Saskia Keesstra, Thomas Panagopoulos, Le Yu and Chuanrong Zhang
Land 2021, 10(12), 1365; https://doi.org/10.3390/land10121365
Received: 20 November 2021 / Revised: 4 December 2021 / Accepted: 8 December 2021 / Published: 10 December 2021
(This article belongs to the Special Issue Land: 10th Anniversary)
Mapping surface ceramics through systematic pedestrian archaeological survey is considered a consistent method to recover the cultural biography of sites within a micro-region. Archaeologists nowadays conduct surface survey equipped with navigation devices counting, documenting, and collecting surface archaeological potsherds within a set of plotted grids. Recent advancements in unmanned aerial vehicles (UAVs) and image processing analysis can be utilised to support such surface archaeological investigations. In this study, we have implemented two different artificial intelligence image processing methods over two areas of interest near the present-day village of Kophinou in Cyprus, in the Xeros River valley. We have applied a random forest classifier through the Google Earth Engine big data cloud platform and a Single Shot Detector neural network in the ArcGIS Pro environment. For the first case study, the detection was based on red–green–blue (RGB) high-resolution orthophotos. In contrast, a multispectral camera covering both the visible and the near-infrared parts of the spectrum was used in the second area of investigation. The overall results indicate that such an approach can be used in the future as part of ongoing archaeological pedestrian surveys to detect scattered potsherds in areas of archaeological interest, even if pottery shares a very high spectral similarity with the surface. View Full-Text
Keywords: potsherds; detection; pedestrian survey; remote sensing archaeology; single shot detector; artificial intelligence; random forest; Google Earth Engine; Cyprus potsherds; detection; pedestrian survey; remote sensing archaeology; single shot detector; artificial intelligence; random forest; Google Earth Engine; Cyprus
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MDPI and ACS Style

Agapiou, A.; Vionis, A.; Papantoniou, G. Detection of Archaeological Surface Ceramics Using Deep Learning Image-Based Methods and Very High-Resolution UAV Imageries. Land 2021, 10, 1365. https://doi.org/10.3390/land10121365

AMA Style

Agapiou A, Vionis A, Papantoniou G. Detection of Archaeological Surface Ceramics Using Deep Learning Image-Based Methods and Very High-Resolution UAV Imageries. Land. 2021; 10(12):1365. https://doi.org/10.3390/land10121365

Chicago/Turabian Style

Agapiou, Athos, Athanasios Vionis, and Giorgos Papantoniou. 2021. "Detection of Archaeological Surface Ceramics Using Deep Learning Image-Based Methods and Very High-Resolution UAV Imageries" Land 10, no. 12: 1365. https://doi.org/10.3390/land10121365

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