Airborne laser scanning or lidar has now been used by archaeologists for twenty years, with many of the first applications relying on data acquired by public agencies seeking to establish baseline elevation maps, mainly in Europe and North America. More recently, several wide-area acquisitions have been designed and commissioned by archaeologists, the most extensive of which cover tropical forest environments in the Americas and Southeast Asia. In these regions, the ability of lidar to map microtopographic relief and reveal anthropogenic traces on the Earth's surface, even beneath dense vegetation, has been welcomed by many as a transformational breakthrough in our field of research. Nevertheless, applications of the method have attracted a measure of criticism and controversy, and the impact and significance of lidar are still debated. Now that wide-area, high-density laser scanning is becoming a standard part of many archaeologists' toolkits, it is an opportune moment to reflect on its position in contemporary archaeological practice and to move towards a code of ethics that is vital for scientific research. The papers in this Special Collection draw on experiences with using lidar in archaeological research programs, not only to highlight the new insights that derive from it but also to cast a critical eye on past practices and to assess what challenges and opportunities remain for developing codes of ethics. Using examples from a range of countries and environments, contributions revolve around three key themes: data management and access; the role of stakeholders; and public education. We draw on our collective experiences to propose a range of improvements in how we collect, use, and share lidar data, and we argue that as lidar acquisitions mature we are well positioned to produce ethical, impactful, and reproducible research using the technique.
Archaeologists often need to date and group artifact types to discern typologies, chronologies, and classifications. For over a century, statisticians have been using classification and clustering techniques to infer patterns in data that can be defined by algorithms. In the case of archaeology, linear regression algorithms are often used to chronologically date features and sites, and pattern recognition is used to develop typologies and classifications. However, archaeological data is often expensive to collect, and analyses are often limited by poor sample sizes and datasets. Here we show that recent advances in computation allow archaeologists to use machine learning based on much of the same statistical theory to address more complex problems using increased computing power and larger and incomplete datasets. This paper approaches the problem of predicting the chronology of archaeological sites through a case study of medieval temples in Angkor, Cambodia. For this study, we have a large dataset of temples with known architectural elements and artifacts; however, less than ten percent of the sample of temples have known dates, and much of the attribute data is incomplete. Our results suggest that the algorithms can predict dates for temples from 821–1150 CE with a 49-66-year average absolute error. We find that this method surpasses traditional supervised and unsupervised statistical approaches for under-specified portions of the dataset and is a promising new method for anthropological inquiry.
Angkor is one of the world’s largest premodern settlement complexes (9th to 15th centuries CE), but to date, no comprehensive demographic study has been completed, and key aspects of its population and demographic history remain unknown. Here, we combine lidar, archaeological excavation data, radiocarbon dates, and machine learning algorithms to create maps that model the development of the city and its population growth through time. We conclude that the Greater Angkor Region was home to approximately 700,000 to 900,000 inhabitants at its apogee in the 13th century CE. This granular, diachronic, paleodemographic model of the Angkor complex can be applied to any ancient civilization.
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