An Open System for Collection and Automatic Recognition of Pottery through Neural Network Algorithms

Abstract

In the last ten years, artificial intelligence (AI) techniques have been applied in archaeology. The ArchAIDE project realised an AI-based application to recognise archaeological pottery. Pottery is of paramount importance for understanding archaeological contexts. However, recognition of ceramics is still a manual, time-consuming activity, reliant on analogue catalogues. The project developed two complementary machine-learning tools to propose identifications based on images captured on-site, for optimising and economising this process, while retaining key decision points necessary to create trusted results. One method relies on the shape of a potsherd; the other is based on decorative features. For the shape-based recognition, a novel deep-learning architecture was employed, integrating shape information from points along the inner and outer profile of a sherd. The decoration classifier is based on relatively standard architectures used in image recognition. In both cases, training the algorithms meant facing challenges related to real-world archaeological data: the scarcity of labelled data; extreme imbalance between instances of different categories; and the need to take note of minute differentiating features. Finally, the creation of a desktop and mobile application that integrates the AI classifiers provides an easy-to-use interface for pottery classification and storing pottery data.

Information Extraction and Machine Learning for Archaeological Texts

“…Within archaeological research, ML is also becoming more popular, and is being used for a wide range of problems. Some examples are the automatic detection of archaeological features in LiDAR (Light Detection And Ranging) data (Verschoofvan der Vaart et al, 2020;Trier et al, 2018), classification of pottery types based on photos (Gualandi et al, 2021;Pawlowicz & Downum, 2021), analysing projectile point typology (Nash & Prewitt, 2016), and differentiating between lithic assemblages (Grove & Blinkhorn, 2020). For a more in depth overview of ML in archaeology and cultural heritage, see Bickler (2021) and Fiorucci et al (2020).…”

Research Ancient Artifact Identification Methods under Intelligent Perception and Recognition Technology

“…The location of all the ceramic bowls in the cave has to be determined. It is possible to evaluate the correlation between artifact classes and bowls [14]. The buffer function helps to evaluate if specific types of cave features were selected for ritual activities.…”

A Comparative Analysis of the Bayesian Regularization and Levenberg–Marquardt Training Algorithms in Neural Networks for Small Datasets: A Metrics Prediction of Neolithic Laminar Artefacts

“…Artificial intelligence (AI), specifically both machine learning (ML) and deep learning (DL), has lately been applied to archaeology in several ways, such as for the identification of sites' locations, their extensions, archaeological artifacts' dispersion in sites [1][2][3][4][5][6], as well as for taxonomic/typological artifact identification [7][8][9].…”