Unauthorized cultural resource alterations range from looting and grave robbing to contract violations and wildland fires. Such alterations degrade cultural resources’ spiritual, communal, ecological, economic, and scientific values. Alterations often violate communal senses of place, security, and belonging. Alterations complicate jurisdiction-specific management, which is premised on up-to-date information on resource sizes, conditions, and significance. Cultural resource damage assessment protocols based on proven forensic practices distil to eight fieldwork steps: verify the alteration, assemble the team, survey the scene, document the evidence, gather the evidence, assess the archaeological value and the cost of repair and restoration, prescribe emergency remediation, and confirm evidence documentation and custody. The eight steps give special consideration to local communities and Indigenous Territories, where unauthorized alterations are as common as they are elsewhere, whereas impacts to spiritual and cultural values are generally greater. Adapted to jurisdiction- and incident-specific circumstances, the steps will guide responses to alterations by community leaders, land managers, regulators, law enforcement agents, and archaeologists, including preparation of excellent damage assessment reports. Damage assessment practitioners and land managers should refine these practices to deter alterations, engage Tribes and other affected communities, halt postalteration degradation, ensure accountability, and enable jurisdiction-scale curation of cultural resources and their unique value constellations.

The transnational networks of the illicit and illegal antiquities trade are hard to perceive. We suggest representing the trade as a knowledge graph with multiple kinds of relationships that can be transformed by a neural architecture into a “knowledge graph embedding model.” The result is that the vectorization of the knowledge represented in the graph can be queried for missing “knowledge” of the trade by virtue of the various entities’ proximity in the multidimensional embedding space. In this article, we build a knowledge graph about the antiquities trade using a semantic annotation tool, drawing on the series of articles in the Trafficking Culture Project's online encyclopedia. We then use the AmpliGraph package, a series of tools for supervised machine learning (Costabello et al. 2019) to turn the graph into a knowledge graph embedding model. We query the model to predict new hypotheses and to cluster actors in the trade. The model suggests connections between actors and institutions hitherto unsuspected and not otherwise present in the original knowledge graph. This approach could hold enormous potential for illuminating the hidden corners of the illicit antiquities trade. The same method could be applied to other kinds of archaeological knowledge.

Software now allows archaeologists to document excavations in more detail than ever before through rich, born-digital datasets. In comparison, paper documentation of past excavations (a valuable corpus of legacy data) is prohibitively difficult to work with. This pilot study explores creating custom software to digitize paper field notes from the 1970s excavations of the Gulkana site into machine-readable text and maps to be compatible with born-digital data from subsequent excavations in the 1990s. This site, located in Alaska's Copper River Basin, is important to archaeological understanding of metalworking innovation by precontact Northern Dene people, but is underrepresented in the literature because no comprehensive map of the site exists. The process and results of digitizing this corpus are presented in hopes of aiding similar efforts by other researchers.

Archaeologists tend to produce slow data that is contextually rich but often difficult to generalize. An example is the analysis of lithic microdebitage, or knapping debris, that is smaller than 6.3 mm (0.25 in.). So far, scholars have relied on manual approaches that are prone to intra- and interobserver errors. In the following, we present a machine learning–based alternative together with experimental archaeology and dynamic image analysis. We use a dynamic image particle analyzer to measure each particle in experimentally produced lithic microdebitage (N = 5,299) as well as an archaeological soil sample (N = 73,313). We have developed four machine learning models based on Naïve Bayes, glmnet (generalized linear regression), random forest, and XGBoost (“Extreme Gradient Boost[ing]”) algorithms. Hyperparameter tuning optimized each model. A random forest model performed best with a sensitivity of 83.5%. It misclassified only 28 or 0.9% of lithic microdebitage. XGBoost models reached a sensitivity of 67.3%, whereas Naïve Bayes and glmnet models stayed below 50%. Except for glmnet models, transparency proved to be the most critical variable to distinguish microdebitage. Our approach objectifies and standardizes microdebitage analysis. Machine learning allows studying much larger sample sizes. Algorithms differ, though, and a random forest model offers the best performance so far.

Recent years have seen the rapid expansion of airborne and spaceborne remote-sensing products adopted by archaeologists for interpreting ancient landscapes and managing heritage resources. A growing and increasingly specialized literature attests to the promise and availability of commercial and publicly funded satellite imagery, as well as UAV-mounted sensors across a range of resolutions and price points. In the South Caucasus (including the countries of Armenia, Azerbaijan, and Georgia), a growing commitment to landscape approaches in archaeology is stimulating the adoption of satellite remote sensing as an important new tool for identifying and managing archaeological resources while tracing the impact of historic land-use alterations in survey areas. Nevertheless, budgetary challenges and a lack of training opportunities among international partners and heritage organizations outside of the funding streams of large academic institutions can lead to widening technological gulfs in the discipline that reinforce colonial relationships. Building on recent technical articles covering specific imagery datasets, this article aims to address this by providing a general review of free or low-cost remotely sensed datasets available to archaeologists, with the aim of broadening awareness of these important tools and their vocabularies, and illustrating them with recent published examples from the South Caucasus.

Despite considerable developments in the archaeological application of lidar for detecting roads, less attention has been given to studying road morphology using lidar. As a result, archaeologists are well equipped to locate but not thoroughly study roads via lidar data. Here, a method that visualizes and statistically compares road profiles using elevation values extracted from lidar-derived digital elevation models is presented and illustrated through a case study on Chaco roads, located in the US Southwest. This method is used to establish the common form of ground-truthed Chaco roads and to measure how frequently this form is across non-ground-truthed roads. This method is an addition to the growing suite of tools for documenting and comparing roads using remotely sensed data, and it can be particularly useful in threatened landscapes where ground truthing is becoming less possible.

In the last decade, archaeologists have been using human-occupied interactive digital built environments to investigate human agency, settlement, and behavior. To document this evidence, we provide here one method of conducting drone-based photogrammetry and GIS mapping from within these digital spaces based on well-established methods conducted in physical landscapes. Mapping is an integral part of archaeology in the natural world, but it has largely eluded researchers in these new, populated digital landscapes. We hope that our proposed method helps to resolve this issue. We argue that employing archaeological methods in digital environments provides a successful methodological framework to investigate human agency in digital spaces for anthropological purposes and has the potential for extrapolating data from human-digital landscape interactions and applying them to their natural analogues.

Geospatial research in archaeology often relies on datasets previously collected by other archaeologists or third-party groups, such as state or federal government entities. This article discusses our work with geospatial datasets for identifying, documenting, and evaluating prehistoric and historic water features in the western United States. As part of a project on water heritage and long-term views on water management, our research has involved aggregating spatial data from an array of open access and semi-open access sources. Here, we consider the challenges of working with such datasets, including outdated or disorganized information, and fragmentary data. Based on our experiences, we recommend best practices: (1) locating relevant data and creating a data organization method for working with spatial data, (2) addressing data integrity, (3) integrating datasets in systematic ways across research cohorts, and (4) improving data accessibility.

Archaeologists have an obligation to conduct research that is relevant and responsive to the desires, interests, values, and concerns of Indigenous descendant communities. Current best practices for collaborative, community-based archaeologies emphasize long-term engagement and “full collaboration,” including the coproduction of knowledge and total stakeholder involvement. The present-day structures and demands of archaeology—especially in CRM and graduate student research contexts—can serve to make such fully collaborative work difficult, if not impossible. Oftentimes, these difficulties result in a complete abdication of collaboration or even consultation beyond the bare minimum required by law. However, professional archaeologists must strive in all instances to work alongside Native communities in respectful, responsive, and mutually beneficial ways even if this work may often fall short of the loftiest ideal. In this article, the authors present two case studies in collaboration from recent projects conducted in the North American midcontinent. These case studies clearly demonstrate how tribal fieldwork monitoring, working with tribal institutional review boards (IRBs), and other related forms of “imperfect” collaboration can still help move us toward a more ethical, inclusive, and respectful future archaeology.

In November 1995, the Laboratory of Archaeology at the University of Georgia submitted inventories and summaries of Indigenous ancestors and funerary objects in its holdings to comply with the passage of the Native American Graves Protection and Repatriation Act (NAGPRA). However, after this submission, the Laboratory attempts at consultation with federally recognized descendant Tribal communities who have cultural ties in the state of Georgia were not successful, and NAGPRA-related activities essentially stalled at the Laboratory. Beginning in 2019, the Laboratory's staff recognized a lack of formal NAGPRA policies or standards, which led to a complete reevaluation of the Laboratory's approach to NAGPRA. In essence, it was the Laboratory's renewed engagement with NAGPRA and descendan tribal communities that became the catalyst for change in the Laboratory's philosophy as a curation repository. This shift in thinking set the Laboratory on a path toward building a descendant community–informed institutional integrity (DCIII) level of engagement with consultation and collaborative efforts in all aspects of collections management and archaeological research. In this article, we outline steps that the Laboratory has taken toward implementing meaningful policies and practices created with descendant Tribal communities that both fulfill and extend bounds of NAGPRA compliance.

Underwater archaeological research has been developed less aggressively in Malaysia than in other ASEAN partner countries, such as Indonesia, Thailand, Vietnam, and the Philippines. In past decades, financial constraints have limited the development of underwater archaeology, and the field has been dominated by commercial salvage experts. Malaysia has not addressed many issues or fundamental problems related to future development. The discovery of the Bidong Shipwreck in 2013 has raised hopes that underwater archaeological research in Malaysia will develop more dynamically. The successful excavation of this shipwreck site proves that local experts can conduct scientific excavations. This article presents and discusses the discovery and process of excavating artifacts from the Bidong Shipwreck. The project outcomes provide a guide for stakeholders and agencies involved in future underwater excavations in Malaysian waters.

As an escape from the day-to-day drudgery of our “real” world, video games allow us to leave reality and explore new fictional worlds. These worlds are more enjoyable when they are developed from various digital materials and presented as an official story, which is called “lore.” Lore invites the player to submerge into background stories of fictional worlds, including but not limited to those in video games. Video-game lore often provides historical narratives of these worlds, sometimes even drawing on archaeological themes in the creation of characters and places. The video game League of Legends (LoL), developed by Riot Games, has its own detailed lore, often revolving around individual characters. Riot has developed this lore not only through the video game itself but through accompanying media. Although the lore is separate from the game and not necessarily needed, the advantage is that it can orient individuals to the world and the background stories of the characters and players. Additionally, it allows people who are not interested in playing the game to interact with the fictional world, given that the stories are captivating on their own. Here, we briefly discuss Riot's interactive lore website and focus on the background of two places within the LoL fictional world: Ionia and Targon. We examine the presentation of these places through an archaeological framework. Then, we evaluate how archaeology is portrayed throughout the lore, focusing specifically on archaeological field notes and the methods that we are told are employed by one of the characters. In our analysis of the portrayal of Targon, the discussion centers on how archaeological perspectives are an integral framework for the LoL lore. Lore creates a sense of place (a connection between a person and a spatial setting) for the fan community, players, and even developers. Consequently, it is useful to focus on this idea of storytelling through lore, taking a critical view of its power and how it can impact various audiences.