Call for Papers #S24 Computational classification in archaeology

Advances in Archaeological Practice, 2021

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.

Journal on Computing and Cultural Heritage, 2014

Formalizing and objectifying the process of artefact classification is an old wish of many archaeologists. On the other hand, data mining in general and machine learning in particular have already inspired many disciplines to introduce new paradigms of data analysis and knowledge discovery. Hence, this article aims for reviving the Typological Debate by adapting approved methods from other fields of science to archaeological data. To this end, we extensively discuss the concept of similarity and assess the suitability of machine learning techniques for the purposes of classification and typology development. Our methodology covers all steps starting from unordered, unlabelled objects to the emergence of a consistent and reusable typology. The application of this process is exemplarily illustrated by classifying the vessels from a Late Bronze Age cemetery in Eastern Saxony. Despite the individual character of these vessels, we achieved class prediction rates of more than 95%. Such a ...

The Encyclopedia of Archaeological Sciences, 2018

Classification has been crucial to the archaeological enterprise from its inception. Its importance ranges from the pragmatic-a way to arrange artifactual material in an orderly manner-to the analytic-a means to construct a "window" opening onto the lifeway of a past group of people inferred from the traces of their behavior expressed through patterning in artifactual material. From a pragmatic perspective, a classification may be based on the material (see chromatography and archaeological materials analysis) from which an artifact is made, its morphological form, its inferred func-tionality, the technology (see archaeologies of technology) underlying its production, the time period of its occurrence (see dating in archaeology), the cultural context in which it occurs (or is believed to occur), its occurrence together with other artifacts, and so on. Archaeological classification, in its most basic sense, involves forming classes of artifacts whose members are considered to be equivalent for analytical and interpretive purposes. In this sense, archaeological classification can be thought of as a division of a collection of objects into disjoint and exhaustive classes-an operation known formally as a partition. The rationale for the divisions may range from being empirical, for example when the division is based on properties of the artifacts, to being conceptual, for example when the division is created using constructs developed as part of the archaeologist's understanding of how the material domain integrated with the lifeway of the producers and users of the artifacts that have been recovered. A classification needs to be rigorous, consistent,​ and replicable.

Sustainability

Code and data sharing are crucial practices to advance toward sustainable archaeology. This article explores the performance of supervised machine learning classification methods for provenancing archaeological pottery through the use of freeware R code in the form of R Markdown files. An illustrative example was used to show all the steps of the new methodology, starting from the requirements to its implementation, the verification of its classification capability and finally, the production of cluster predictions. The example confirms that supervised methods are able to distinguish classes with similar features, and provenancing is achievable. The provided code contains self-explanatory notes to guide the users through the classification algorithms. Archaeometrists without previous knowledge of R should be able to apply the novel methodology to similar well-constrained classification problems. Experienced users could fully exploit the code to set up different combinations of param...

Europen Journal Of Post-Classical Archaeologies (PCA), 2022

Although some of the now popular deep learning techniques and technologies have a relatively long history (nearly 30 years), it has been in the last 5 years when these applications have reached mainstream Archaeology, cultural heritage and museum studies. There is a new conscience of data processing in Archaeology, although the nature of these data hardly arrives to the usual label ‘big data’, and it has opened the methodological toolbox at use, especially in domains like reconstruction, remote sensing, object recognition, typological analysis, and collection management and visitor studies. In this paper, the history and current applications of neural networks and related methods of machine learning in archaeology, cultural heritage and museum studies are investigated. The necessary theoretical background on induction and learning is provided to understand the possibilities and limitations of computational techniques. Keywords: archaeology, cultural heritage, artificial intelligence, neural networks, deep learning.

Master thesis, 2022

Artefact classification is one of the main themes and an important practice since the beginnings of archaeology, while machine learning (ML) became one of the most efficient approaches to increase our knowledge in a number of disciplines. This research describes a ML model developed for the classification of pottery assemblages, identifying its benefits and limitations, focusing on the importance of artefacts features for the identification of vessel shape classes, the relations among theses classes, and to what extent this kind of knowledge can be used to replicate classifications made by experts.

Within the field of archaeological predictive modelling, a range of techniques and methods are possible. However, given the amount of archaeological data now available, it may be necessary to develop new methods and to rethink methods currently in use. Specifically, methods that make use of correlations within the datasets should be explored. This paper compares the potential of mathematical and statistical methods to use existing data for archaeological prediction and analyses.

Archeologia e Calcolatori, 31.2, 2020

Since the 1970s, the development of archaeological databases has characterised the history of archaeological computing. The paper presents a summary of the pivotal early projects, with a particular focus on Italy and France, up to the current projects shared online. They are constantly monitored by the international journal Archeologia e Calcolatori, that since 1990 is an observatory of theoretical and methodological aspects of computing and information technology applied to archaeology.

New Europe College Yearbook 2001-2002, 2005

Editor: Irina Vainovski-Mihai CONTENTS NEW EUROPE COLLEGE 7 AXINIA CRASOVSCHI RUSSIAN OLD BELIEVERS (LIPOVANS) IN ROMANIA: CULTURAL VALUES AND SYMBOLS 15 ANCA CRIVÃÞ LE MERVEILLEUX DANS LES ENCYCLOPÉDIES LATINES MÉDIÉVALES 61 CONSTANÞA GHIÞULESCU HOMMES ET FEMMES DEVANT LA JUSTICE L'EXEMPLE DE LA VALACHIE AU XVIII e SIÈCLE 95 LUMINIÞA MUNTEANU MARGINAUX ET MARGINALITÉ DANS L'EMPIRE OTTOMAN 165 NONA-DANIELA PALINCAª ON CLASSIFICATION IN ARCHAEOLOGY 217 LAURA PAMFIL LES SOURCES GRECQUES DE L'ONTOLOGIE DE CONSTANTIN NOICA 249 COSIMA RUGHINIª ROMA COMMUNITIES IN DEVELOPMENT PROJECTS 287 DIANA STANCIU COERCIVE AUTHORITY AND POPULAR SOVEREIGNTY IN MARSIGLIO OF PADUA'S DEFENSOR PACIS 319 LEVENTE SZABÓ THE MAKING OF THE NINETEENTH-CENTURY HUNGARIAN HISTORICAL CANON 353 BOGDAN TÃTARU-CAZABAN VISIONS DE LA HIÉRARCHIE CÉLESTE AU XIII e SIÈCLE 401 MIRCEA VASILESCU LE DISCOURS ANTI-OCCIDENTAL DANS LA PRESSE ROUMAINE POST-COMMUNISTE 435 * 2000-2001 Fellow. As her paper was not ready to be published in the yearbook of her series, it was introduced into the present issue.

Archaeologists generally agree that high-power computer technology constitutes the most efficient venue for addressing many issues in archaeological research. Digital techniques have become indispensable components of archaeological surveys, fieldwork, lab work, and communication between researchers. One of the greatest advantages of the digital approach is its ability to examine large assemblages of items using advanced statistical methods. Digital documentation has reached the point of no return in archaeological research, and reverting to traditional methods is highly improbable. However , digital data may also contain additional information that has yet to be extracted by computer analysis. In this arena, new computer algorithms can be triggered by research questions that cannot be addressed without digital models.