We report the results of drone lidar survey at a high-elevation archaeological site in the Chachapoyas region of Peruvian Amazonia. Unlike traditional airborne remote sensing, drone lidar produces very high-density measurements at a wide range of scan angles by operating at low altitudes and slow flight speeds. These measurements can resolve near vertical surfaces and novel dimensions of variability in architectural datasets. We show in a case study at Kuelap that the number of detected structures almost exactly matches the number reported from previous ground level surveys, and we use these data to quantify the relative circularity and size frequency distribution of architectural structures. We demonstrate variability in domestic architecture that was obscured in previous models produced using low-resolution remote sensing. Spatial analysis of these attributes produces new hypotheses about the site’s construction history and social organization.

Big data have arrived in archaeology, in the form of both large-scale datasets themselves and in the analytics and approaches of data science. Aerial data collected from satellite-, airborne- and UAV-mounted sensors have been particularly transformational, allowing us to capture more sites and features, over larger areas, at greater resolution, and in formerly inaccessible landscapes. However, these new means of collecting, processing, and visualizing datasets also present fresh challenges for archaeologists. What kinds of questions are these methods suited to answer, and where do they fall short? How do they articulate with the work of collecting smaller scale and lower resolution data? How are our relationships with “local” communities impacted by working at the scales of entire provinces, nation-states, and continents? This themed issue seeks to foster a conversation about how the unprecedented expansion of archaeological site detection, the globalization of archaeological data structures and databases, and the use of high-resolution aerial datasets are changing both the way archaeologists envision the past and the way we work in the present. In our introduction to the issue, presented here, we outline a series of conceptual and ethical issues posed by big data approaches in archaeology and provide an overview of how the nine essays that comprise this volume each address them.

Archaeologists study many phenomena that scale beyond even our most geographically expansive field methodologies. The promise of collecting archaeologically relevant data beyond the scale of regional surveys is among the most exciting prospects of the “data revolution.” Yet previous efforts have either struggled to generate high-quality data within expansive regions or to use well-edited interregional datasets to address novel research questions. We discuss the development of two collaborative research projects that seek to address these problems—GeoPACHA (Geospatial Platform for Andean Culture, History and Archaeology) and LOGAR (Linked Open Gazetteer of the Andean Region). The former is an online platform facilitating virtual archaeological survey of satellite and historical aerial imagery; the latter collates primary source information on Andean places. We illustrate the potential of both tools through presentation and analysis of a comprehensive basemap of the planned colonial towns built during a mass resettlement program instituted in the viceroyalty of Peru in the 1570s C.E.

Archaeological surveys conducted through the inspection of high-resolution satellite imagery promise to transform how archaeologists conduct large-scale regional and supra-regional research. However, conducting manual surveys of satellite imagery is labour- and time-intensive, and low target prevalence substantially increases the likelihood of miss-errors (false negatives). In this article, the authors compare the results of an imagery survey conducted using artificial intelligence computer vision techniques (Convolutional Neural Networks) to a survey conducted manually by a team of experts through the Geo-PACHA platform (for further details of the project, see Wernke et al. 2023). Results suggest that future surveys may benefit from a hybrid approach—combining manual and automated methods—to conduct an AI-assisted survey and improve data completeness and robustness.