ID:
publications-2210
Type:
Peer reviewed articles
Year:
2020
Authors:
Hector A. Orengo, Francesc C. Conesa, Arnau Garcia-Molsosa, AgustÃn Lobo, Adam S. Green, Marco Madella, Cameron A. Petrie
Title:
Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data
Venue/Journal:
Proceedings of the National Academy of Sciences
DOI:
10.1073/pnas.2005583117
Research type:
Uncategorized
Water System:
Groundwater
Technical Focus:
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.
Link with Projects:
746446
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