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Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data

Orengo, Hector A., Conesa, Francesc C., Garcia-Molsosa, Arnau, Lobo, Agustín, Green, Adam S., Madella, Marco, Petrie, Cameron A.·2020·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.2005583117PMC PMC7414161
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Abstract

Significance: This paper illustrates the potential of machine learning-based classification of multisensor, multitemporal satellite data for the remote detection and mapping of archaeological mounded settlements in arid environments. Our research integrates multitemporal synthetic-aperture radar and multispectral bands to produce a highly accurate probability field of mound signatures. The results largely expand the known concentration of Indus settlements in the Cholistan Desert in Pakistan ( ca . 3300 to 1500 BC), with the detection of hundreds of new sites deeper in the desert than previously suspected including several large-sized (>30 ha) urban centers. These distribution patterns have major implications regarding the influence of climate change and desertification in the collapse of the largest of the Old-World Bronze Age civilizations.

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  • To both the lay person and automated computer algorithms, it can be difficult or impossible to distinguish between archaeological and modern features.

    “To both the lay person and automated computer algorithms, it can be difficult or impossible to distinguish between archaeological and modern features [22, 28, 30].”

    From Unstructured satellite survey detects up to 20% of archaeological sites in coastal valleys of southern Peru (2024) · cites this paper as [28]

  • The most effective machine learning programs are still outperformed by teams of experts manually combing through large volumes of high resolution satellite imagery.

    “However, these types of automated satellite surveys have a number of limitations, and previous research demonstrates that the most effective machine learning programs are still outperformed by teams of experts manually combing through large volumes of high resolution satellite imagery [28, 29].”

    From Unstructured satellite survey detects up to 20% of archaeological sites in coastal valleys of southern Peru (2024) · cites this paper as [28]

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Metadata

Authors
Orengo, H. A., Conesa, F. C., Garcia-Molsosa, A., Lobo, A., Green, A. S., Madella, M., & Petrie, C. A.
Affiliations
  • Landscape Archaeology Research Group (GIAP), Catalan Institute of Classical Archaeology, 43003 Tarragona, Spain;
  • Institute of Earth Sciences Jaume Almera, Spanish National Research Council, 08028 Barcelona, Spain;
  • McDonald Institute for Archaeological Research, University of Cambridge, CB2 3ER Cambridge, United Kingdom;
  • Culture and Socio-Ecological Dynamics, Department of Humanities, Universitat Pompeu Fabra, 08005 Barcelona, Spain;
ORCID
Venue
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Published
2020-07-20
Volume
117
Issue
31
Pages
18240-18250
ISSN
  • 0027-8424
  • 1091-6490
DOI
10.1073/pnas.2005583117
PMC
PMC7414161
Cited by
165
References
0
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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