气候变化领域集成服务门户(CCPortal): Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data

CCPortal

DOI	10.1073/pnas.2005583117
	Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data
	Orengo H.A.; Conesa F.C.; Garcia-Molsosa A.; Lobo A.; Green A.S.; Madella M.; Petrie C.A.
发表日期	2020
ISSN	0027-8424
起始页码	18240
结束页码	18250
卷号	117 期号:31
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 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. © 2020 National Academy of Sciences. All rights reserved.
英文关键词	Archaeology; Indus civilization; Machine learning; Multitemporal and multisensor satellite big data; Virtual constellations
语种	英语
scopus关键词	algorithm; archeology; article; big data; civilization; classifier; desert; Pakistan; probability; remote sensing; telecommunication; water availability
来源期刊	Proceedings of the National Academy of Sciences of the United States of America (IF:9.58[JCR-2018],10.6[5-Year])
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/160853
作者单位	Orengo, H.A., Landscape Archaeology Research Group (GIAP), Catalan Institute of Classical Archaeology, Tarragona, 43003, Spain; Conesa, F.C., Landscape Archaeology Research Group (GIAP), Catalan Institute of Classical Archaeology, Tarragona, 43003, Spain; Garcia-Molsosa, A., Landscape Archaeology Research Group (GIAP), Catalan Institute of Classical Archaeology, Tarragona, 43003, Spain; Lobo, A., Spanish National Research Council, Institute of Earth Sciences Jaume Almera, Barcelona, 08028, Spain; Green, A.S., McDonald Institute for Archaeological Research, University of Cambridge, Cambridge, CB2 3ER, United Kingdom; Madella, M., Department of Humanities, Culture and Socio-Ecological Dynamics, Universitat Pompeu Fabra, Barcelona, 08005, Spain, Catalan Institution for Research and Advanced Studies, Barcelona, 08010, Spain, School of Geography,Archaeology and Environmental Studies, University of Witwatersrand, Johannesburg, 2000, South Africa; Petrie, C.A., McDonald Institute for Archaeological Research, Uni...
推荐引用方式 GB/T 7714	Orengo H.A.,Conesa F.C.,Garcia-Molsosa A.,et al. Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data[J],2020,117(31).
APA	Orengo H.A..,Conesa F.C..,Garcia-Molsosa A..,Lobo A..,Green A.S..,...&Petrie C.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).
MLA	Orengo H.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).