Arch-I-Scan: Automated recording and machine learning for collating Roman ceramic tablewares and investigating eating and drinking practices

Global challenge. The value of our social and cultural heritage cannot be overestimated as it affects the direction of our development as humans. How to understand and preserve it is a fundamental question in modern science. Centuries of archaeological investigation has produced millions of artefacts that are part of this heritage and are classified and analysed by human experts, presenting a huge challenge to process and interpret all these remains. We aim to meet this challenge using artificial intelligence to create an unprecedented system for automated artefact classification and collation which can be used by non-specialists, allowing experts to focus on analysing these artefacts for greater understandings of the past. Specific challenge. The Roman period is exceptionally rich archaeologically. The millions of artefacts from across the Roman world are infinitely more informative about people's lives than iconic monuments. However, these artefacts are currently under-utilised in studies of social practices - particularly the wealth of ceramic tablewares used by almost everyone from senator to slave. This under-utilisation is largely due to these artefacts' extensiveness and inherent difficulties in recording them all. For decades ceramics have been recorded and analysed selectively (e.g. only diagnostic sherds of specific vessel types) for chronological sequencing of individual excavations, or for investigating trade patterns between regions, rather than to answer socio-cultural questions, e.g. how were particular vessels used and in what circumstances. More comprehensive recording can facilitate consumption-oriented analyses for new levels of understanding of varying social practices among the diverse communities that made up the Roman world. This project develops a state-of-the art image-recognition and machine-learning service, Arch-I-Scan, a proof-of-concept experiment of which was successfully carried out for the AHRC network, 'Big Data on the Roman Table'. We will train this service and develop its machine-learning capacity on 100,000s of Roman tableware remains in extensive collections from different social and regional contexts in Roman Britain - London, Colchester and its environs, Vindolanda (on Hadrian's Wall), and Leicestershire. Machine training will move from recording complete/near complete vessels to more fragmentary remains. Used on handheld devices (e.g. mobile phones) by non-specialists and specialists, Arch-I-Scan will automatically recognise and record details of pottery remains and digitally collate and store large quantities of data. Roman tableware remains, often from large-scale production centres (e.g. samian ware from South Gaul), constitute some of the most easily recognisable and extensive bodies of archaeological data with high levels of similarity, in ranges of forms and fabric types, across a wide geographical area. Thus, besides being crucial evidence for Roman food- and drink-consumption practices in different social contexts in Britain, the selected material comprises an excellent body of artefacts to ensure wider application of Arch-I-Scan at other Roman sites, in Britain and beyond. Once Arch-I-Scan is sufficiently trained and has recorded and 'learned' to classify the artefacts from these collections, the resulting datasets will be made freely available for other archaeologists to use as comparanda in their own analyses. This can lead to more comprehensive analyses across Roman archaeology for more socially-oriented questions. Arch-I-Scan can continue to 'learn' from these and other types of pottery as well as other archaeological artefacts. Greater knowledge of how the micro-histories of objects - and the 'human-thing entanglements' of their micro-archaeological contexts - play important roles in our understandings of socio-cultural practice in the context of Roman history can also transform material-cultural approaches to social practice in global history.