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Title:
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Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data
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Author:
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Orengo Romeu, Hector A.; Conesa, Francesc C.; Garcia i Molsosa, Arnau; Lobo, Agustín; Green, Adam S.; Madella, Marco; Petrie, Cameron A.
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Abstract:
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This paper presents an innovative multisensor, multitemporal machine-learning approach using remote sensing big data for
the detection of archaeological mounds in Cholistan (Pakistan). The Cholistan Desert presents one of the largest concentrations
of Indus Civilization sites (from ca. 3300 to 1500 BC). Cholistan has figured prominently in theories about changes in water availability, the rise and decline of the Indus Civilization, and the transformation of fertile monsoonal alluvial plains into an extremely
arid margin. This paper implements a multisensor, multitemporal machine-learning approach for the remote detection of archaeological mounds. A classifier algorithm that employs a large-scale collection of synthetic-aperture radar and multispectral images has been implemented in Google Earth Engine, resulting in an accurate probability map for mound-like signatures across an area that covers ca. 36,000 km2. The results show that the area presents many more archaeological mounds than previously recorded, extending south and east into the desert, which has major implications for understanding the archaeological significance of the region. The detection of small (<5 ha) to large mounds (>30 ha) suggests that there were continuous shifts in settlement location. These shifts are likely to reflect responses to a dynamic and changing hydrological network and the influence of the progressive northward advance of the desert in a long-term process that culminated in the abandonment of much of the settled area during the Late Harappan period. |
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Publication date:
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2020-07-10 |
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Subject (UDC):
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90 - Arqueologia. Prehistòria |
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Subject(s):
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Índia -- Arqueologia
Arqueologia del paisatge -- Índia
Intel·ligència computacional
Imatges satel·litàries |
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Rights:
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This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) |
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Pages:
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11 p. |
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Document type:
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Article
Article - Published version |
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DOI:
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10.1073/pnas.2005583117
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Published by:
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PNAS
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Publish at:
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Proceedings of the National Academy of Sciences of the United States of America, 117 (2020), p. 18240-18250
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