Automatic ceramic identification using machine learning. Lusitanian amphorae and Faience. Two Portuguese case studies
Datos Bibliográficos
| ID | 19553702 |
|---|---|
| Autores | Joel Santos (0000-0002-5796-9213, autor de correspondencia), Diogo A P Nunes (0000-0002-6614-8556, Instituto Superior Técnico), Ruslan Padnevych (Universidade Nova de Lisboa), José Carlos Quaresma (0000-0003-3139-1975, University of Lisbon), Martim Lopes (0000-0001-9261-7240, University of Lisbon), Joana Gil (0000-0001-9301-6797, University of Lisbon), João Pedro Bernardes (0000-0002-4091-5833, University of Algarve), Tânia Manuel Casimiro (0000-0002-9471-6194, University of Lisbon) |
| Año | 2024 |
| Volumen | 10 |
| Número | 1 |
| Fecha de publicación | 2024-12-31 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | STAR Science & Technology of Archaeological Research (JOURNAL) |
| Identificadores de la revista | ISSN: 2054-8923 • E-ISSN: 2054-8923 |
| Editorial | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/20548923.2024.2343214 |
| OpenAlex | W4396975644 |
| Idioma | EN |
| Citas recibidas | 1 |
| Referencias citadas | 28 |
This article presents a novel approach to classifying archaeological artefacts using machine learning, specifically deep learning, rather than relying on traditional, time-consuming human-based methods. By employing Convolutional Neural Networks (CNNs), this approach aims to expedite and enhance the identification process, making it more accessible to a wider audience. The study focuses on two types of artefacts- Roman Lusitanian amphorae (2nd-5th centuries) and Portuguese faience (16th-18th centuries)- chosen for their diversity. While Lusitanian amphorae lack decoration, Portuguese faience poses challenges with subtle colour variations. The study demonstrates the potential of this approach to overcome these hurdles. The paper outlines the methodology, dataset creation, and model training, emphasizing the importance of extensive data and computational resources. The ultimate objective of this research is to develop a mobile application that utilizes image classification techniques to accurately classify ceramic sherds and bring about a significant transformation in archaeological classification
Archaeology · Convolutional neural network · Geography · Portuguese · 3D Surveying and Cultural Heritage · Archaeological Research and Protection · Computer Science · Cultural Heritage Materials Analysis · Artificial Intelligence · Ecology
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| Obras citantes distintas | 1 |
|---|---|
| Citas por año | 1 |
| Intervalo de citas | 2025 - 2025 (1) |
| Velocidad de citación | recent |
| Altamente citado | No |
| Tipos de cita | Neutras: 1 |