干旱区生态环境知识资源中心(Arid): Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data

Arid

DOI	10.1073/pnas.2005583117
	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, Agustin; Green, Adam S.; Madella, Marco; Petrie, Cameron A.
通讯作者	Orengo, HA ; Conesa, FC
来源期刊	PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
ISSN	0027-8424
出版年	2020
卷号	117 期号:31 页码:18240-18250
英文摘要	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 km(2). 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.
英文关键词	multitemporal and multisensor satellite big data machine learning archaeology Indus Civilization virtual constellations
类型	Article
语种	英语
开放获取类型	hybrid, Green Published, Green Submitted
收录类别	SSCI
WOS记录号	WOS:000573679600021
WOS关键词	THAR DESERT ; SETTLEMENT ; RIVER ; SAR ; LANDSCAPES ; GUJARAT ; IMAGERY ; CORONA ; SITES ; INDIA
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/326558
作者单位	[Orengo, Hector A.; Conesa, Francesc C.; Garcia-Molsosa, Arnau] Catalan Inst Class Archaeol, Landscape Archaeol Res Grp GIAP, Tarragona 43003, Spain; [Lobo, Agustin] Spanish Natl Res Council, Inst Earth Sci Jaume Almera, Barcelona 08028, Spain; [Green, Adam S.; Petrie, Cameron A.] Univ Cambridge, McDonald Inst Archaeol Res, Cambridge CB2 3EF, England; [Madella, Marco] Univ Pompeu Fabra, Dept Humanities, Culture & Socioecol Dynam, Barcelona 08005, Spain; [Madella, Marco] Catalan Inst Res & Adv Studies, Barcelona 08010, Spain; [Madella, Marco] Univ Witwatersrand, Sch Geog Archaeol & Environm Studies, ZA-2000 Johannesburg, South Africa; [Petrie, Cameron A.] Univ Cambridge, Dept Archaeol, Cambridge CB2 3DZ, England
推荐引用方式 GB/T 7714	Orengo, Hector A.,Conesa, Francesc C.,Garcia-Molsosa, Arnau,et al. Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data[J],2020,117(31):18240-18250.
APA	Orengo, Hector A..,Conesa, Francesc C..,Garcia-Molsosa, Arnau.,Lobo, Agustin.,Green, Adam S..,...&Petrie, Cameron A..(2020).Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data.PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA,117(31),18240-18250.
MLA	Orengo, Hector A.,et al."Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data".PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 117.31(2020):18240-18250.