Automated Detection of Archaeological Mounds Using Machine-Learning Classification of Multisensor and Multitemporal Satellite Data
Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data Hector A. Orengoa,1, Francesc C. Conesaa,1, Arnau Garcia-Molsosaa, Agustín Lobob, Adam S. Greenc, Marco Madellad,e,f, and Cameron A. Petriec,g aLandscape Archaeology Research Group (GIAP), Catalan Institute of Classical Archaeology, 43003 Tarragona, Spain; bInstitute of Earth Sciences Jaume Almera, Spanish National Research Council, 08028 Barcelona, Spain; cMcDonald Institute for Archaeological Research, University of Cambridge, CB2 3ER Cambridge, United Kingdom; dCulture and Socio-Ecological Dynamics, Department of Humanities, Universitat Pompeu Fabra, 08005 Barcelona, Spain; eCatalan Institution for Research and Advanced Studies, 08010 Barcelona, Spain; fSchool of Geography, Archaeology and Environmental Studies, The University of the Witwatersrand, Johannesburg 2000, South Africa; and gDepartment of Archaeology, University of Cambridge, CB2 3DZ Cambridge, United Kingdom Edited by Elsa M. Redmond, American Museum of Natural History, New York, NY, and approved June 25, 2020 (received for review April 2, 2020) This paper presents an innovative multisensor, multitemporal mounds (7–9). Georeferenced historical map series have also machine-learning approach using remote sensing big data for been used solely or in combination with contemporary declas- the detection of archaeological mounds in Cholistan (Pakistan). sified data (10–14). In recent years, RS-based archaeological The Cholistan Desert presents one of
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