Developing the ArchAIDE application: A digital workflow for identifying, organising and sharing archaeological pottery using automated image recognition

Anichini, Francesca; Banterle, Francesco; Garrigós, Jaume Buxeda i; Callieri, Marco; Dershowitz, Nachum; Dubbini, Nevio; Díaz, Diego Lucendo; Evans, Tim; Gattiglia, Gabriele; Green, Katie; Gualandi, Maria Letizia; Hervas, Miguel Angel; Itkin, Barak; Fernández, Marisol Madrid i; Gascón, Eva Miguel; Remmy, Michael; Richards, Julian D.; Scopigno, Roberto; Vila, Llorenç; Wolf, Lior; Wright, Holly; Zallocco, Massimo

doi:10.11141/ia.52.7

Cited by 18 publications

(9 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the advent of Machine Learning techniques, such separation is routinely possible, using iterative methodologies that improve on their results through validation of reliable training data. The utility of such approaches has been seen more widely in Archaeology, including towards remote sensing and prediction or classification of archaeological sites 11 – 13 , the recording and creation of artefact typologies 14 – 19 , and more recently for lithic sourcing 20 – 24 . For this latter topic, these techniques promise more powerful approaches to the separation of geological samples and increased accuracy over classical statistical techniques.…”

Section: Introductionmentioning

confidence: 99%

Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain

Elliot

Morse

Smythe³

et al. 2021

Sci Rep

View full text Add to dashboard Cite

It is 50 years since Sieveking et al. published their pioneering research in Nature on the geochemical analysis of artefacts from Neolithic flint mines in southern Britain. In the decades since, geochemical techniques to source stone artefacts have flourished globally, with a renaissance in recent years from new instrumentation, data analysis, and machine learning techniques. Despite the interest over these latter approaches, there has been variation in the quality with which these methods have been applied. Using the case study of flint artefacts and geological samples from England, we present a robust and objective evaluation of three popular techniques, Random Forest, K-Nearest-Neighbour, and Support Vector Machines, and present a pipeline for their appropriate use. When evaluated correctly, the results establish high model classification performance, with Random Forest leading with an average accuracy of 85% (measured through F1 Scores), and with Support Vector Machines following closely. The methodology developed in this paper demonstrates the potential to significantly improve on previous approaches, particularly in removing bias, and providing greater means of evaluation than previously utilised.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain

Elliot

Morse

Smythe³

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Both these aspects are crucial to the potential application of machine learning to domains where there are no perfect datasets. As compared to some other solutions to the problem of classifying archaeological ceramics, such as the ArchAIDE project [ 16 , 55 , 56 ], we do not rely on the user’s input beyond the photograph. As such, we aim to include expert knowledge in training the classifier only, so that as little expertise as possible is required from any potential end user, making any eventual tool more widely usable.…”

Section: Discussionmentioning

confidence: 99%

“…The list of points is used by each procedure to draw an axial section, which is rotated to generate a 3D mesh of points that corresponds to the rotationally symmetric shape of each vessel. Figure 6 shows a diagram of this process (see [16,55,56] for comparable processes). As we have previously discussed, almost all photographed real pots have at least some degree of damage.…”

Section: Pot Simulationsmentioning

confidence: 99%

Learning from Scarce Information: Using Synthetic Data to Classify Roman Fine Ware Pottery

Jareño

Helden

Mirkes

et al. 2021

Entropy

View full text Add to dashboard Cite

In this article, we consider a version of the challenging problem of learning from datasets whose size is too limited to allow generalisation beyond the training set. To address the challenge, we propose to use a transfer learning approach whereby the model is first trained on a synthetic dataset replicating features of the original objects. In this study, the objects were smartphone photographs of near-complete Roman terra sigillata pottery vessels from the collection of the Museum of London. Taking the replicated features from published profile drawings of pottery forms allowed the integration of expert knowledge into the process through our synthetic data generator. After this first initial training the model was fine-tuned with data from photographs of real vessels. We show, through exhaustive experiments across several popular deep learning architectures, different test priors, and considering the impact of the photograph viewpoint and excessive damage to the vessels, that the proposed hybrid approach enables the creation of classifiers with appropriate generalisation performance. This performance is significantly better than that of classifiers trained exclusively on the original data, which shows the promise of the approach to alleviate the fundamental issue of learning from small datasets.

show abstract

“…The archaeological research on material objects is nowadays more and more supported by digital image analysis (DIA) and this trend is always growing with the new possibilities offered also by open-source software [37]. Different routines and methods-from 2 to 3D digital imaging techniques-are applied on a variety of records, from small objects to whole excavated areas to keep track of their characteristics and extract qualitative and quantitative data [15,7,10,14,22,20,23,2,32].…”

Section: Introductionmentioning

confidence: 99%

Digital image analysis on cathodoluminescence microscopy images for ancient ceramic classification: methods, applications, and perspectives

et al. 2022

View full text Add to dashboard Cite

This paper aims to investigate the application of cathodoluminescence (CL) imagery (with optical microscopy) and CL spectroscopy (with Scanning Electron Microscope) in ancient ceramics studies, for a proper classification of petro-fabrics and mineral inclusions based on their CL response. Digital image analysis (DIA) routines are proposed for both qualitative and quantitative analysis, and outcomes are reviewed in the light of classical optical microscopy (OM) and scanning electron microscopy (SEM–EDS) approaches. Obtained results demonstrated the suitability of CL in discriminating ceramic groups, offering also complementary information connected with aspects as provenance or manufacturing processes. CL imagery and spectroscopy data are also used to discuss perspectives of the method, debating bias, limits, or suspicious inconsistencies, exploring how compositional features or sample’s nature imprint CL response, and finally providing the basis for future developments. Addendum at the back of the paper includes and extensively discusses some technical aspects regarding the applied DIA routines, developed in open-sources software environments and available as supplementary materials.

show abstract

Developing the ArchAIDE application: A digital workflow for identifying, organising and sharing archaeological pottery using automated image recognition

Cited by 18 publications

References 17 publications

Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain

Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain

Learning from Scarce Information: Using Synthetic Data to Classify Roman Fine Ware Pottery

Digital image analysis on cathodoluminescence microscopy images for ancient ceramic classification: methods, applications, and perspectives

Contact Info

Product

Resources

About