Ancient lowland Maya complexity as revealed by airborne laser scanning of northern Guatemala

Canuto, Marcello A.; Estrada-Belli, Francisco; Garrison, Thomas G.; Houston, Stephen D.; Acuña, Mary Jane; Kováč, Milan; Marken, Damien; Nondédéo, Philippe; Auld-Thomas, Luke; Castanet, Cyril; Chatelain, David; Chiriboga, Carlos; Drápela, Tomáš; Lieskovský, Tibor; Tokovinine, Alexandre; Velásquez, Antolín; Fernández-Diaz, Juan Carlos; Shrestha, Ramesh

doi:10.1126/science.aau0137

Cited by 267 publications

(219 citation statements)

References 77 publications

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“…In Central America, they have employed airborne laser scanning (LiDAR) to discover archaeological sites beneath the dense forest canopy (Canuto et al. ; see also Stenborg, Schaan, and Figueiredo ). Archaeologists are also realizing the analytical and illustrative promise of geographic information systems (GIS) and drone photogrammetry, employing them in tandem to systematically map architecture, model terrain, and examine relationships between spatial variables (Hixon et al.…”

Section: Methodological and Epistemological Advancesmentioning

confidence: 99%

New Digs: Networks, Assemblages, and the Dissolution of Binary Categories in Anthropological Archaeology

Kosiba

2019

American Anthropologist

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Anthropological archaeology has long been a process of categorization. The history of the subdiscipline could be rendered in terms of an ongoing project to create, critique, and then refashion the categories by which archaeologists understand and explain social and ecological processes. Despite ever-changing theoretical tides, many archaeologists have continued to adhere to a lingua franca that demarcates their object of study, whether "the past," "nature," complexity, material culture, or historical knowledge. In 2018, strong currents in archaeological scholarship have both challenged and washed out the boundaries of many of these epistemological mainstays. This was more than a postmodern exercise to question metanarratives of history or to destabilize supposed social universals. The recent scholarship develops innovative theoretical perspectives and sophisticated methodological tools to shed light on the varied human interactions, practices, and projects that were previously occluded by research too sharply focused on bounded categories such as periods, polities, adaptations, and meanings. Furthermore, archaeologists are now more than ever critically examining the colonial institutions that they perpetuate and seeking to establish new common ground with Indigenous communities. A decidedly archaeological perspective seems to be taking shape.This is a view that sees social life from the ground up in terms of complicated relationships between people, things, and other organisms that cannot be reduced to catchall categories. [year in review, archaeology, archaeological theory, current issues, Indigenous archaeology, environmental anthropology] RESUMEN La arqueología antropológica ha sido durante mucho tiempo un proceso de categorización. La historia de la subdisciplina podría ser descrita en términos de un proyecto continuo de crear, criticar y luego rehacer las categorías a través de las cuales los arqueólogos entienden y explican los procesos sociales y ecológicos. A pesar de las mareas teóricas siempre cambiantes, muchos arqueólogos han continuado adhiriéndose a una lengua franca que demarca su objeto de estudio, ya sea "el pasado", "naturaleza", complejidad, cultura material o conocimiento histórico.En 2018, corrientes fuertes en la investigación arqueológica han tanto retado como difuminado los límites de muchos de los pilares epistemológicos. Esto fue más que un ejercicio posmoderno para cuestionar metanarrativas de la historia o para desestabilizar supuestos universales sociales. La reciente investigación desarrolla perspectivas teóricas innovadoras y herramientas metodológicas sofisticadas para arrojar luz sobre las variadas interacciones humanas, prácticas y proyectos que estuvieron previamente ocluidos por investigación excesivamente enfocada en categorías delimitadas tales como períodos, unidades políticas, adaptaciones y significados. Además, los arqueólogos ahora están más que nunca examinando críticamente las instituciones coloniales que perpetúan y buscando establecer un nuevo terreno común con las c...

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Section: Methodological and Epistemological Advancesmentioning

confidence: 99%

New Digs: Networks, Assemblages, and the Dissolution of Binary Categories in Anthropological Archaeology

Kosiba

2019

American Anthropologist

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“…Lidar is one of the more commonly-used tools by archaeologists allowing for high-resolution site-based imaging. Lidar has furthered archaeological research across the world and often in incredible ways, revealing detailed features on the land surface long obscured by forest canopy (Canuto et al 2018;Chase et al 2012;Evans et al 2013;Lasaponara, Coluzzi, and Masini 2012;Liebmann et al 2016;McCoy, Asner, and Graves 2011). The majority of these efforts have focused on complex, stratified, sedentary societies with substantial archaeological features like buildings, monuments and terraces.…”

Section: An Iterative Approach To High-resolution Site-based and Landmentioning

confidence: 99%

Remotely Sensed Big Data and Iterative Approaches to Cultural Feature Detection and Past Landscape Process Analysis

Howey

Sullivan

Burg

et al. 2020

Journal of Field Archaeology

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The concept of "big" data is nothing new to archaeologists; we have long made a profession of collecting, organizing, and analyzing a surfeit of data describing everything from minute artifact attributes to landscape-wide environmental characteristics. Regardless of this abundance, we have and continue to confront the self-same problem inherent in "big" data, namely what analyses will actually help us use these data to advance understandings of past human behaviors. With burgeoning remote sensing technologies archaeology faces a new wave of "big" data, but how do these techniques improve our ability to make the inferential leaps to bridge the present to the past and bring new insights forward? We argue that, to date, remote sensing techniques (satellite, aerial, and unpersonned aerial imagery) have been applied somewhat narrowly to mostly high-resolution site-based research in archaeology. To truly unleash the capabilities of these techniques, and expand our capacity for wrangling "big" data to more fully investigate past patterns, we need to conduct iterative analyses incorporating remotely sensed data on bounded archaeological sites and regions and unbounded landscapes. A case study from the Late Precontact (ca. A.D. 1200-1600) period in the northern Great Lakes of North America detailing how such an iterative approach can be initiated is explored here.

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“…Remote sensing (RS) provides a rapid and low-cost way of exploring, mapping and monitoring archaeological features of interest (AOIs) across the world [29]. Research that involves the identification of AOIs increasingly employs aerial photographs and spy satellite images [30][31][32][33] as well as multispectral and hyperspectral imagery [34][35][36][37][38], SAR data [39][40][41][42], and LiDAR products [43][44][45]. RS has unique advantages for detecting the large archaeological sites such as the Silk Road, Grand Canal, Nasca Lines and Great Wall [4].…”

Section: Archaeological Remote Sensingmentioning

confidence: 99%

Identifying Linear Traces of the Han Dynasty Great Wall in Dunhuang Using Gaofen-1 Satellite Remote Sensing Imagery and the Hough Transform

et al. 2019

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The Han Dynasty Great Wall (GH), one of the largest and most significant ancient defense projects in the whole of northern China, has been studied increasingly not only because it provides important information about the diplomatic and military strategies of the Han Empire (206 B.C.–220 A.D.), but also because it is considered to be a cultural and national symbol of modern China as well as a valuable archaeological monument. Thus, it is crucial to obtain the spatial pattern and preservation situation of the GH for next-step archaeological analysis and conservation management. Nowadays, remote sensing specialists and archaeologists have given priority to manual visualization and a (semi-) automatic extraction approach is lacking. Based on the very high-resolution (VHR) satellite remote sensing imagery, this paper aims to identify automatically the archaeological features of the GH located in ancient Dunhuang, northwest China. Gaofen-1 (GF-1) data were first processed and enhanced after image correction and mathematical morphology, and the M-statistic was then used to analyze the spectral characteristics of GF-1 multispectral (MS) data. In addition, based on GF-1 panchromatic (PAN) data, an auto-identification method that integrates an improved Otsu segmentation algorithm with a Linear Hough Transform (LHT) is proposed. Finally, by making a comparison with visual extraction results, the proposed method was assessed qualitatively and semi-quantitatively to have an accuracy of 80% for the homogenous background in Dunhuang. These automatic identification results could be used to map and evaluate the preservation state of the GH in Dunhuang. Also, the proposed automatic approach was applied to identify similar linear traces of other generations of the Great Wall of China (Western Xia Dynasty (581 A.D.–618 A.D.) and Ming Dynasty (1368 A.D.–1644 A.D.)) in various geographic regions. Moreover, the results indicate that the computer-based automatic identification has great potential in archaeological research, and the proposed method can be generalized and applied to monitor and evaluate the state of preservation of the Great Wall of China in the future.

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Ancient lowland Maya complexity as revealed by airborne laser scanning of northern Guatemala

Cited by 267 publications

References 77 publications

New Digs: Networks, Assemblages, and the Dissolution of Binary Categories in Anthropological Archaeology

New Digs: Networks, Assemblages, and the Dissolution of Binary Categories in Anthropological Archaeology

Remotely Sensed Big Data and Iterative Approaches to Cultural Feature Detection and Past Landscape Process Analysis

Identifying Linear Traces of the Han Dynasty Great Wall in Dunhuang Using Gaofen-1 Satellite Remote Sensing Imagery and the Hough Transform

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