1. Introduction

Every day, archaeologists are working to discover and tell stories using objects from the past, investing considerable time, effort and funding to identify and characterise individual finds. Pottery is of fundamental importance for the comprehension and dating of archaeological contexts, and in order to understand the dynamics of production, trade flows, and social interactions. Today, characterisation and classification of ceramics are carried out manually, through the expertise of specialists and the use of analogue catalogues held in archives and libraries. While not seeking to replace the knowledge and expertise of specialists, the ArchAIDE project worked to optimise and economise identification processes, developing a new system that streamlines the practice of pottery recognition in archaeology, using the latest automated image recognition technology. At the same time, ArchAIDE worked to ensure archaeologists remained at the heart of the decision-making process within the identification workflow, and focused on optimising tasks that were repetitive and time consuming. Specifically, ArchAIDE worked to support the essential classification and interpretation work of archaeologists (during both fieldwork and post-excavation analysis) with an innovative app for tablets and smartphones.

The ArchAIDE project was funded by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement N.693548, with a consortium of partners representing both the academic and industry-led ICT domains, and the academic and development-led archaeology domains. The archaeological partners within the consortium were the MAPPA Lab at the University of Pisa (coordinator) which has relevant experience in mathematical and digital applications in archaeology and archaeological communication; the Material Culture and Archaeometry research unit at the University of Barcelona, which is focused on promoting studies of material culture, especially relating to archaeological ceramics and archaeometric approaches; the Digital Archaeology Laboratory at the University of Cologne, which manages ARACHNE, a highly structured object database in partnership with the German Archaeological Institute (DAI); and the Archaeology Data Service (ADS) at the University of York, which is a world-leading digital data archive for archaeology. The consortium also includes two companies carrying out rescue and development-led archaeological investigations: Baraka Arqueólogos S.L., which has particular expertise in the study of archaeological ceramics, and Elements S.L. which is experienced in the application of digital technologies related to ceramic studies. Finally, the consortium's technical ICT partners were the Visual Computing Lab at CNR-ISTI, an institute of Italian CNR devoted to research on Visual Media and Cultural Heritage; the Deep Learning Lab at the School of Computer Science at Tel Aviv University, which focuses on document analysis, image textual description, and action recognition; and a private software company, Inera s.r.l., which has development experience in the field of protocols and web apps.

The collaborative work of the archaeological and technical partners created a pipeline where potsherds are photographed, their characteristics compared against a trained neural network, and the results returned with suggested matches from a comparative collection with typical pottery types and characteristics. Once the correct type is identified, all relevant information for that type is linked to the new sherd and stored within a database that can be shared online. This goal has been implemented through the creation of the following practical elements:

Figure 1: Overview video of the ArchAIDE workflow (2 mins 21 seconds) Taken from ArchAIDE consortium (2019)

The underlying technologies developed for the app were also implemented as a desktop application, which is a web-based, real-time data visualisation resource, to improve access to archaeological heritage and generate new understanding. Both the desktop application and the app can also be used as a tool to aid learning about pottery identification, either for students or when specialists are not available.

1.1 Methodology, specification and design

As suggested by (Orton and Hughes 2013, 48), 'wherever possible it is much better to have a finds specialist at hand on site'. The specialist can give instant advice and identification, allowing (1) dating and (2) quantification. Dating allows the formulation of working hypotheses for the phasing of a site, whereas quantification is a way to record general pottery characteristics, i.e. the proportions of the different ceramic types that comprise an assemblage. This allows archaeologists to interpret and understand the dynamics of production, trade flows, social interactions, etc. Pottery recognition requires complex skills, is heavily dependent on human inspection and interpretation, while also being a very time-consuming and often repetitive activity.

To address the time-consuming and repetitive aspects of pottery recognition, the ArchAIDE system was designed to support a more automated pottery identification workflow, meeting real user needs and reducing time and costs within both academic and professional archaeology (Figure 2). Although similar in methodological approach, the goals, costs, and types of available expertise between academic and professional archaeology can vary tremendously. The first is less bound by time pressures, and typically allows more time for post-excavation documentation. The second, often related to development-led archaeology, is usually closely bound by time pressures, and often carried out by teams composed of professional archaeologists, where specialist training and/or access to specialist knowledge is at a premium.

As a discipline, archaeology is often an early adopter of novel technologies, but on the whole, maintains a conservative approach when it comes to replacing well-established methods. As such, ArchAIDE has been careful to support good practice, rather than seek to change existing workflows, and the ArchAIDE system was designed to follow specialist methodologies: recognising a pottery type via observation of the profile/cut section shape, and/or through analysis of the decorative surface treatment.

As both speed and accuracy are important in pottery identification, the ArchAIDE system was designed to support the classification and interpretation work of archaeologists (during both fieldwork and post-excavation analysis) through the creation of an innovative app designed for mobile devices and desktop computers. The app features an interface that facilitates the identification of a potsherd from a photograph, acquired using a camera from a typical mobile device. Within the app, the ArchAIDE system supports efficient and powerful algorithms for pottery characterisation. This allows search and retrieval of the possible visual/geometric correspondences against a complex database built from comparative data, derived from digital and analogue resources. In order to create the database, and the training data for the deep learning image-recognition algorithms, it was necessary to acquire robust comparative data for the specific pottery types chosen for the ArchAIDE proof-of-concept.

Figure 2
Figure 2: Illustration of the basic ArchAIDE workflow

The ArchAIDE database is composed of (1) a Reference Database and (2) a Results Database. The Reference Database contains a number of born-digital and digitised paper catalogues of pottery typologies, which have been combined to create a coherent comparative resource. The Reference Database was designed with a core catalogue, containing basic information about pottery classes and types that are common across different thematic and period-based pottery catalogues, with additional media (drawings, photographs, 3D models etc.) to aid the main recognition application. The Reference Database includes spatial data related to each type entity to allow data analysis and the creation of data visualisation, such as distribution maps.

To create the Reference Database, in addition to openly licensed resources (e.g. Roman Amphorae: a digital resource, held by the ADS; University of Southampton 2014), use of materials protected by copyright (e.g. paper catalogues) and/or by sui generis right (e.g. digital resource, held by the University of Cologne) required meticulous analysis of European InfoSoc Directive (Information Society Directive 2001/29/EC of 22 May 2001 on the harmonisation of certain aspects of copyright and related rights in the information society) and Database Directive (Directive 96/9/EC of 11 March 1996 on the legal protection of databases). While use of the necessary resources was possible thanks to the research exceptions justified for non-commercial purposes, copyright issues proved problematic for any commercial exploitation of the app, even when photography of a sherd or digitisation of resources in a paper catalogue were carried out by the ArchAIDE project itself.

The Results Database was intended to facilitate the capture of user-generated text and images in the field/laboratory. Once a sherd is identified using the automated image recognition algorithm against the reference dataset, the diagnostic information matched to the type of sherd was then associated with the sherd and stored within a larger 'results dataset' which records the classes and types, to create a 'digital assemblage'. Search and retrieval is based on shape-based and appearance-based deep learning algorithms for classification. In summary, the workflow of the ArchAIDE App is: download onto a mobile device; take pictures of a pottery sherd using the device's built-in camera; edit its known attributes and classify it using comparative images returned using the automated image recognition functionality provided within the app; and save the relevant information about the sherd into a digital assemblage associated with a particular archaeological site or intervention. This workflow combines technological innovation while retaining the well-established methodologies used by archaeologists and the ability to agree or disagree with the classification, with the potential to generate economic benefits by reducing time spent and costs.


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