Three-dimensional (3D) artifact modeling is becoming an increasingly utilized tool in archaeology. In comparison with other methods of 3D scanning, photogrammetry has the benefits of being relatively inexpensive, mobile, and more adaptable for use in field conditions. As part of a larger project to document variability in lithic production systems across the Middle to Upper Paleolithic Transition in Western Europe, we developed a photography rig for the express purpose of systematically capturing images for the creation of 3D photogrammetric models. This low-cost rig greatly streamlines both the photo-taking and post-processing stages of model creation. Additional tips relating to the coating of difficult-to-capture objects with a mineral spray are also provided. Three-dimensional models of lithic cores from the Châtelperronian, Protoaurignacian, and Early Aurignacian levels of the site of Les Cottés (France) are presented as examples of the quality of model that can be produced using this system.

While a long history of experimental data shows that aerial thermal images can reveal a wide range of both surface and subsurface archaeological features, technological hurdles have largely prevented more widespread use of this promising prospecting method. However, recent advances in the sophistication of thermal cameras, the reliability of commercial drones, and the growing power of photogrammetric software packages are revolutionizing archaeologists' ability to collect, process, and analyze aerial thermal imagery. This paper provides an overview of the theory behind aerial thermography in archaeology, as well as a discussion of an emerging set of methods developed by the authors for undertaking successful surveys. Summarizing investigations at archaeological sites in North America, the Mediterranean, and the Near East, our results illustrate some contexts in which aerial thermography is very effective, as well as cases in which ground cover, soil composition, or the depth and character of archaeological features present challenges. In addition, we highlight novel approaches for filtering out noise caused by vegetation, as well as methods for improving feature visibility using radiometric thermal imagery.

The use of airborne LiDAR (Light Detection and Ranging) in western Belize, Central America, has revolutionized our understanding of the spatial dynamics of the ancient Maya. This technology has enabled researchers to successfully demonstrate the large-scale human modifications made to the ancient tropical landscape, providing insight on broader regional settlement. Before the advent of this laser-based technology, heavily forested cover prevented full coverage and documentation of Maya sites. Mayanists could not fully recover or document the extent of ancient occupation and could never be sure how representative their mapped and excavated samples were relative to ancient settlement. Employing LiDAR in tropical and subtropical environments, like that of the Maya, effectively provides ground, as well as forest cover information, leading to a much fuller documentation of the complexities involved in the ancient human-nature interface. Airborne LiDAR was first flown over a 200 km2 area of the archaeological site of Caracol, Belize, in April 2009. In April and May 2013 an additional 1,057 km2 were flown with LiDAR, permitting the contextualization of the city of Caracol within its broader region and polity. The use of this technology has transformed our understanding of regional archaeology in the Maya area.

Luminescence dating provides a direct age estimate of the time of last exposure of quartz or feldspar minerals to light or heat and has been successfully applied to deposits, rock surfaces, and fired materials in a number of archaeological and geological settings. Sampling strategies are diverse and can be customized depending on local circumstances, although all sediment samples need to include a light-safe sample and material for dose-rate determination. The accuracy and precision of luminescence dating results are directly related to the type and quality of the material sampled and sample collection methods in the field. Selection of target material for dating should include considerations of adequacy of resetting of the luminescence signal (optical and thermal bleaching), the ability to characterize the radioactive environment surrounding the sample (dose rate), and the lack of evidence for post-depositional mixing (bioturbation in soils and sediment). Sample strategies for collection of samples from sedimentary settings and fired materials are discussed. This paper should be used as a guide for luminescence sampling and is meant to provide essential background information on how to properly collect samples and on the types of materials suitable for luminescence dating.

Machine learning (ML) is rapidly being adopted by archaeologists interested in analyzing a range of geospatial, material cultural, textual, natural, and artistic data. The algorithms are particularly suited toward rapid identification and classification of archaeological features and objects. The results of these new studies include identification of many new sites around the world and improved classification of large archaeological datasets. ML fits well with more traditional methods used in archaeological analysis, and it remains subject to both the benefits and difficulties of those approaches. Small datasets associated with archaeological work make ML vulnerable to hidden complexity, systemic bias, and high validation costs if not managed appropriately. ML's scalability, flexibility, and rapid development, however, make it an essential part of twenty-first-century archaeological practice. This review briefly describes what ML is, how it is being used in archaeology today, and where it might be used in the future for archaeological purposes.

With the ever expanding quantity of high-resolution aerial and satellite imagery available to archaeologists, numerous researchers have sought to address this “big data” challenge by developing automated methods to aid in the discovery and mapping of archaeological sites and features. This paper reviews several notable efforts to create automated discovery tools, including both spectral and object-based approaches, and highlights the difficulties these projects have encountered. Arguing instead for the critically important role of a human analyst in archaeological discovery, I illustrate interim results of an ongoing project that utilizes CORONA satellite imagery to document previously unknown sites in a 300,000 km2 study area in the northern Fertile Crescent. The project is based on what I term “brute force” methods, relying on systematic exploration of imagery by trained analysts, and has now successfully created a database of more than 14,000 sites, some 10,000 of which are previously undocumented. Results of the project highlight the need for human intervention to make any archaeological discovery meaningful, suggesting that imagery analysis, like any act of archaeological investigation, requires an engaged, thoughtful and creative scholar.

Since initial applications of strontium isotope human sourcing in the early 1990s, the use of the method has steadily increased in archaeology and in anthropology more broadly. Despite this trend, the collection of necessary baseline environmental data has not been standardized and sometimes does not occur at all. A thorough environmental sampling strategy will ensure that all the variability within a selected region is documented, which is a critical step to improving the accuracy of sourcing studies. Furthermore, shared strontium baseline data collections are needed to improve the intercomparability of datasets and results. This paper provides a case study from a semiarid region in northwestern New Mexico, USA, highlighting the need for a bottom-up approach to baseline data collection (from bedrock to animal) and describes the methods of pre-field planning and collecting, including rationales for what samples to collect for Sr isotope baseline data. The authors hope that this paper will lay a foundation for the implementation and standardization of Sr isotope baseline data collecting, which does not currently exist.

The logistics of time-efficient yet accurate documentation of archaeological features are a challenge within the context of pedestrian survey. Here we present results of two case studies documenting the use of photogrammetry under field conditions within the Great Plains. Results demonstrate the ease with which high quality models can be obtained with minimal training and by using standard field cameras and computers. Different models of pit hearth and architectural features are presented to demonstrate the versatility of model output formats in terms of their accessibility to broad audiences and the variety of display options available. Comparison with more traditional field measurements indicates the suitability and superiority of this approach in terms of time investment and mapping detail and accuracy. Combined, these efforts demonstrate the potential of this technique to be incorporated into the standard practices of academic and professional field projects with minimal interruption.

Issues of social and economic inequality are increasingly becoming areas of study for archaeologists; however, little work has been done on the uniform application of analytical methods. Here we assess the use of the Gini index for determining wealth inequality in eight Prehispanic central Mexican contexts. We analyze house size at the Late Postclassic sites of Capilco (village), Cuexcomate (town), and Yautepec (city), all in the state of Morelos. Agricultural field sizes for two communities described in a Nahuatl-language census immediately after the Spanish conquest are also analyzed. Our final context is the Xolalpan phase apartment compounds at Teotihuacan. Using these case studies we discuss methodological issues concerning the use of Gini indices to infer social inequality with archaeological data. We find that the Gini index, when applied following the proposed methodological standards, serves as a useful tool for the quantification of inequality across multiple archaeological case studies.

In April 2009, a lidar survey flown by the National Center for Airborne Laser Mapping recorded 200 square kilometers of terrain that comprised the Classic Period Maya city of Caracol, Belize. The data revealed a highly manipulated landscape of dense settlement, agricultural terraces, and residential reservoirs. Literature on Maya agriculture has discussed the benefits of terraces in controlling soil erosion, retaining water, and managing the gravitational flow of water; however, until now these benefits have not been quantified or demonstrated on the ground at scale. This research utilizes these lidar data and data derivatives in order to test the degree to which the ancient Maya manipulated their environment and were able to support large-scale populations through their landscape management practices. As such, the research provides evidence supporting the significance of agricultural terraces and their impact on limiting soil erosion, increasing water retention, and permitting flow control over rainfall runoff. This research also highlights the conscious effort by the ancient Maya to manage the hydrology of their terraced landscape.

Recently declassified photographs taken by U2 spy planes in the 1950s and 1960s provide an important new source of historical aerial imagery useful for Eurasian archaeology. Like other sources of historical imagery, U2 photos provide a window into the past, before modern agriculture and development destroyed many archaeological sites. U2 imagery is older and in many cases higher resolution than CORONA spy satellite imagery, the other major source of historical imagery for Eurasia, and thus can expand the range of archaeological sites and features that can be studied from an aerial perspective. However, there are significant barriers to finding and retrieving U2 imagery of particular locales, and archaeologists have thus not yet widely used it. In this article, we aim to reduce these barriers by describing the U2 photo dataset and how to access it. We also provide the first spatial index of U2 photos for the Middle East. A brief discussion of archaeological case studies drawn from U2 imagery illustrates its merits and limitations. These case studies include investigations of prehistoric mass-kill hunting traps in eastern Jordan, irrigation systems of the first millennium BC Neo-Assyrian Empire in northern Iraq, and twentieth-century marsh communities in southern Iraq.

Least Cost Analysis (LCA) is a geospatially focused quantitative approach that can help archaeologists better understand how people may have moved across a landscape. At its core is the assumption that humans make decisions about movement as fully rational actors with complete knowledge of the landscape, attempting to minimize the cost of that movement as they travel from one location to another. LCA is most often used to construct a small number of hypothetical routes between locations of interest and, when used effectively, can even lead to the creation of entire transportation networks. Desktop GIS software packages, both commercial and open source, make running LCA relatively straightforward, but that does not necessarily mean that the output is informative or accurate. The following tutorial is designed to expose the reader to the foundational steps required to complete an LCA workflow, along with the scientific rationale behind each step and how to avoid potential pitfalls that one might run into along the way. Relevant literature is cited throughout the tutorial in order to provide context for the technical methods that underpin available tools, to suggest various ways that LCA can be used to solve archaeological problems, and to show the art of the possible.

Preservation of digital data is predicated on the expectation of its reuse, yet that expectation has never been examined within archaeology. While we have extensive digital archives equipped to share data, evidence of reuse seems paradoxically limited. Most archaeological discussions have focused on data management and preservation and on disciplinary practices surrounding archiving and sharing data. This article addresses the reuse side of the data equation through a series of linked questions: What is the evidence for reuse, what constitutes reuse, what are the motivations for reuse, and what makes some data more suitable for reuse than others? It concludes by posing a series of questions aimed at better understanding our digital engagement with archaeological data.

How do archaeologists share their research data, if at all? We review what data are, according to current influential definitions, and previous work on the benefits, costs, and norms of data sharing in the sciences broadly. To understand data sharing in archaeology, we present the results of three pilot studies: requests for data by e-mail, review of data availability in published articles, and analysis of archaeological datasets deposited in repositories. We find that archaeologists are often willing to share but that discipline-wide sharing is patchy and ad hoc. Legislation and mandates are effective at increasing data sharing, but editorial policies at journals lack adequate enforcement. Although most of the data available at repositories are licensed to enable flexible reuse, only a small proportion of the data are stored in structured formats for easy reuse. We present some suggestions for improving the state of date sharing in archaeology; among these is a standard for citing datasets to ensure that researchers making their data publicly available receive appropriate credit.

This study proposes a sampling method for ground-truthing LiDAR-derived data that will allow researchers to verify or predict the accuracy of results over a large area. Our case study is focused on a 24 km2 area centered on the site of Yaxnohcah in the Yucatan Peninsula. This area is characterized by a variety of dense tropical rainforest and wetland vegetation zones with limited road and trail access. Twenty-one 100 x 100 m blocks were selected for study, which included examples of several different vegetation zones. A pedestrian survey of transects through the blocks was conducted, recording two types of errors. Type 1 errors consist of cultural features that are identified in the field, but are not seen in the digital elevation model (DEM) or digital surface model (DSM). Type 2 errors consist of features that appear to be cultural when viewed on the DEM or DSM, but are caused by different vegetative features. Concurrently, we conducted an extensive vegetation survey of each block, identifying major species present and heights of stories. The results demonstrate that the lidar survey data are extremely reliable and a sample can be used to assess data accuracy, fidelity, and confidence over a larger area.

Archaeological data and research results are essential to addressing such fundamental questions as the origins of human culture; the origin, waxing, and waning of civilizations and cities; the response of societies to long-term climate changes; and the systemic relationships implicated in human-induced changes in the environment. However, we lack the capacity for acquiring, managing, analyzing, and synthesizing the data sets needed to address important questions such as these. We propose investments in computational infrastructure that would transform archaeology’s ability to advance research on the field’s most compelling questions with an evidential base and inferential rigor that have heretofore been impossible. At the same time, new infrastructure would make archaeological data accessible to researchers in other disciplines. We offer recommendations regarding data management and availability, cyberinfrastructure tool building, and social and cultural changes in the discipline. We propose funding synthetic case studies that would demonstrate archaeology’s ability to contribute to transdisciplinary research on long-term social dynamics and serve as a context for developing computational tools and analytical workflows that will be necessary to attack these questions. The case studies would explore how emerging research in computer science could empower this research and would simultaneously provide productive challenges for computer science research.

Accurate and high-resolution airborne light detection and ranging (lidar) data have become increasingly important for the discovery and visualization of complete archaeological settlement systems in the Maya Lowlands. We present the results of systematic quantitative analysis of lidar data and ground verification for the major centers of Cahal Pech, Baking Pot, and Lower Dover in the Belize Valley. The Belize Valley is characterized by high density populations living in growing modern towns and villages, and by large-scale agricultural production. This urban environment presents a challenge to reconnaissance efforts since modern construction and agricultural activities have destroyed ancient ruins and created new vegetation patterns. Lidar data was analyzed within a GIS using the Topographic Position Index (TPI) to identify the location of possible archaeological remains. Small-scale, site-level TPI analysis helped identify more detailed archaeological features including small house mounds, terraces, and ditches. Results indicate that lidar data recorded for areas with dense vegetation (e.g., low brush and secondary regrowth) may be less reliable for identification of archaeological remains. The quantitative and qualitative differences between spatial analyses and pedestrian survey results among land cover types indicate that traditional settlement pattern study methods, including pedestrian survey, remain vital to ground-truthing all types of spatial data.

This paper serves a dual purpose. First it is an introduction that aims to frame a set of papers that describe and discuss the process of co-creation in a variety of archaeological projects. We discuss the challenge of community engagement in public archaeology and offer co-creative practice as a method for improving our relationships with descendant communities and the general public. We begin by providing a definition of public archaeology and a brief overview of its evolution over the last few decades. Second, we discuss co-creation’s origins and utilization in the museum and business sectors, where the process is applied to address challenges similar to those archaeologists face. We then demonstrate how co-creation fits into the public/applied archaeological framework. We argue that co-creation must be both co (that is, share power in some way) and creative (that is, not just do the same things better, but do something new). Within this framework, we discuss how co-creation aligns with and informs current trends in public archaeology practice drawing from the case studies included in this issue. We conclude that co-creation has an important place on the collaborative continuum and can help our discipline become more responsive to the needs of our many publics.

Hundreds of thousands of archaeological investigations in the United States conducted over the last several decades have documented a large portion of the recovered archaeological record in the United States. However, if we are to use this enormous corpus to achieve richer understandings of the past, it is essential that both CRM and academic archaeologists change how they manage their digital documents and data over the course of a project and how this information is preserved for future use. We explore the nature and scope of the problem and describe how it can be addressed. In particular, we argue that project workflows must ensure that the documents and data are fully documented and deposited in a publicly accessible, digital repository where they can be discovered, accessed, and reused to enable new insights and build cumulative knowledge.

In 2014, the Southeastern Archaeological Conference (SEAC) conducted a sexual harassment survey of its membership. The survey's goal was to investigate whether sexual harassment had occurred among its members, and if so, to document the rate and demographics of harassment. Our findings include a high (66%) level of harassment, primarily among women, with an additional 13% of respondents reporting sexual assault. This article provides an overview of the survey and responses. Additionally, we analyze survey data aimed at capturing change over time in harassment and assault, correlation between field and non-field tasks and harassment and assault, and correlation between gender of supervisor and harassment and assault. We also discuss the effects of harassment and assault on careers. We conclude with suggestions for decreasing the rate of harassment and assault and urge professional archaeological organizations to document sexual harassment and assault to mitigate the effects on their members and on the discipline as a whole.