Band 64, Heft 6 - Digital Archaeology / Guest Editor: Hubert Mara, Host Editor: Paul Molitor

Rapid progress in digitisation and computer techniques have enabled noteworthy new pottery analysis applications in recent decades. We focus on analytical techniques directed specifically at archaeological pottery research in this survey and review the specific benefits these have brought in the field. We consider techniques based on heterogeneous sources such as drawings, photographs, 3D scans and CT volume data. The various approaches and methods are structured according to the main steps in pottery processing in archaeology: documentation, classification and retrieval. Within these categories we review the most relevant papers and identify their advantages and limitations. We evaluate both freely and commercially available analysis tools and databases. Finally, we discuss open problems and future challenges in the field of pottery analysis.

In Greek art, the phase from 900 to 700 BCE is referred to as the Geometric period due to the characteristically simple geometry-like ornamentations appearing on painted pottery surfaces during this era. Distinctive geometric patterns are typical for specific periods, regions, workshops as well as painters and are an important cue for archaeological tasks, such as dating and attribution. To date, these analyses are mostly conducted with the support of information technology. The primitives of an artefact’s ornamentation can be generally classified into a set of distinguishable pattern classes, which also appear in a similar fashion on other objects. Although a taxonomy of known pattern classes is given in subject-specific publications, the automatic detection and classification of surface patterns from object depictions poses a non-trivial challenge. Our long-term goal is to provide this classification functionality using a specifically designed and trained neural network. This, however, requires a large amount of labelled training data, which at this point does not exist for this domain context. In this work, we propose an effective annotation system, which allows a domain expert to interactively segment and label parts of digitized vessel surfaces. These user inputs are constantly fed back to a Convolutional Neural Network (CNN), enabling the prediction of pattern classes for a given surface area with ever increasing precision. Our work paves the way for a fully automatic classification and analysis of large surface pattern collections, which, with the help of suitable visual analysis techniques, can answer research questions like pattern variability or change over time. While the capability of our proposed annotation pipeline is demonstrated at the example of two characteristic Greek pottery artefacts from the Geometric period, the proposed methods can be readily adopted for the patternation in any other chronological periods as well as for stamped motifs.

Archaeologists are interested in better understanding matters of our human past based on material culture. The tools we use to approach archaeological research questions range from the trowel and brush to, more recently, even those of artificial intelligence. As access to computing technology has increased over time, the breadth of computer-assisted methods in archaeology has also increased. This proliferation has provided us a considerable toolset towards engaging both new and long-standing questions, especially as interdisciplinary collaboration between archaeologists, computer scientists, and engineers continues to grow. As an example of an archaeological project engaging in computer-based approaches, the Guadalupe/Colón Archaeological Project is presented as a case study. Project applications and methodologies range from the regional-scale identification of sites using a geographic information system (GIS) or light detection and ranging (LiDAR) down to the microscopic scale of classifying ceramic materials with convolutional neural networks. Methods relating to the 3D modeling of sites, features, and artifacts and the benefits therein are also explored. In this paper, an overview of the methods used by the project is covered, which includes 1) predictive modeling using a GIS slope analysis for the identification of possible site locations, 2) structure from motion (SfM) drone imagery for site mapping and characterization, 3) airborne LiDAR for site identification, mapping, and characterization, 4) 3D modeling of stone features for improved visualization, 5) 3D modeling of ceramic artifacts for more efficient documentation, and 6) the application of deep learning for automated classification of ceramic materials in thin section. These approaches are discussed and critically considered with the understanding that interdisciplinary cooperation between domain experts in engineering, computer science, and archaeology is an important means of improving and expanding upon digital methodologies in archaeology as a whole.

The use of airborne LiDAR data has become an essential component of landscape archaeology. This review article provides an understandable introduction to airborne LiDAR data processing specific to archaeology with a holistic view from a technical perspective. It is aimed primarily at researchers, students, and experts whose primary field of study is not archaeology. The article first outlines what the archaeological interest in airborne LiDAR data is and how the data processing workflow is archaeology-specific. The article emphasises that the processing workflow is riddled with archaeology-specific details and presents the key processing steps. These are, in order of their impact on the final result, enhanced visualisation, manual reclassification, filtering of ground points, and interpolation. If a single most important characteristic of airborne LiDAR data processing for archaeology is to be emphasised, it is that archaeologists need an archaeology-specific DEM for their work.

Boundaries at different scales or in different cultural contexts vary in permeability, fuzziness, and continuity. The contribution of methods from the Geographical Information Systems (GIS) toolbox used in archaeology for the understanding of boundaries is explored in a case study based on known boundaries that are located in a hilly region east of Cologne, Germany. The known political and religious boundaries date back to the 16th and early 17th century. Furthermore, a linguistic boundary traversing the study area is well-known. A classification of the reliability of the political boundaries is presented based on evidence from different sources including archaeological sites. A traditional GIS method for delimiting territories is Voronoi polygon generation, also known as Thiessen polygons. A variant uses least-cost distances, accounting for costs of movement. A site catchment of a settlement consists of all locations that can be reached by expending a given cost limit. Both the initial Voronoi computations as well as the site catchments failed to reconstruct a significant proportion of the known past boundaries. Therefore, the association of the known boundaries with rivers, ridges, and modern parish boundaries was tested and found significant. In contrast, poor soils and voids identified by least-cost Kernel Density Estimation (LC-KDE) of 16th century settlement locations are no preferred locations of past boundaries. Boundaries of Voronoi polygons based on modified cost models taking the association of past boundaries with ridges, rivers, and modern boundaries into account reconstructed about 27 % of the confirmed past political boundaries successfully. The association of the linguistic boundary with the features mentioned above is insignificant. Diagnostic place names are the traits forming the basis of Voronoi diagrams and LC-KDE applied in archaeological studies for reconstructing boundaries. Considering the uneven distribution and fairly low number of traits, the results of these two approaches are satisfactory.

Digital circuits are widely utilized in computers, because they provide models for various digital components and arithmetic operations. Arithmetic circuits are a subclass of digital circuits that are used to execute Boolean algebra. To avoid problems like the infamous Pentium FDIV bug, it is critical to ensure that arithmetic circuits are correct. Formal verification can be used to determine the correctness of a circuit with respect to a certain specification. However, arithmetic circuits, particularly integer multipliers, represent a challenge to current verification methodologies and, in reality, still necessitate a significant amount of manual labor. In my dissertation we examine and develop automated reasoning approaches based on computer algebra, where the word-level specification, modeled as a polynomial, is reduced by a Gröbner basis inferred by the gate-level representation of the circuit. We provide a precise formalization of this reasoning process, which includes soundness and completeness arguments and adds to the mathematical background in this field. On the practical side we present an unique incremental column-wise verification algorithm and preprocessing approaches based on variable elimination that simplify the inferred Gröbner basis. Furthermore, we provide an algebraic proof calculus in this thesis that allows obtaining certificates as a by-product of circuit verification in order to boost confidence in the outcomes of automated reasoning tools. These certificates can be efficiently verified with independent proof checking tools.