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PyPotteryLens

An open-source deep learning framework for automated digitisation of archaeological pottery documentation

Datos Bibliográficos

ID4616282
AutoresL Cardarelli (0000-0002-2436-9967, autor de correspondencia)
Año2025
Volumen38
Páginase00452
Fecha de publicación2025-09-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaDigital Applications in Archaeology and Cultural Heritage (JOURNAL)
Identificadores de la revistaISSN: 2212-0548
EditorialElsevier BV (PUBLISHER)
DOI10.1016/j.daach.2025.e00452
OpenAlexW4413103224
IdiomaEN
Referencias citadas21

Archaeology · Documentation · Geography · Open source · Pottery · 3D Surveying and Cultural Heritage · Archaeological Research and Protection · Computer Science · Engineering · Image Processing and 3D Reconstruction · Software

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    Open Access•William Y Adams, Ernest W Adams•Archaeological Typology and…•1991

  • Approaches to Archaeological Ceramics

    Open Access•Carla M Sinopoli•Approaches to Archaeological…•1991

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

    Open Access•Maria Letizia Gualandi, Gabriele Gattiglia et al.•Heritage•2021

  • The automatic recognition of ceramics from only one photo

    Open Access•Francesca Anichini, Nachum Dershowitz et al.•Journal of Archaeological Science…•2021

  • Machine Learning Arrives in Archaeology

    Open Access•Simon H Bickler•Advances in Archaeological Practice•2021

  • Dealing with Legacy Data - an introduction

    Open Access•Penelope M Allison•Internet Archaeology•2008

  • Deep learning-based detection of qanat underground water distribution systems using Hexagon spy satellite imagery

    Open Access•Nazarij Buławka, Hector A Orengo et al.•Journal of Archaeological Science•2024

  • Unsupervised clustering of Roman potsherds via Variational Autoencoders

    Open Access•Simone Parisotto, Ninetta Leone et al.•Journal of Archaeological Science•2022

  • Convolutional neural networks for archaeological site detection – Finding “princely” tombs

    Open Access•Gino Caspari, Pablo Crespo•Journal of Archaeological Science•2019

  • A deep variational convolutional Autoencoder for unsupervised features extraction of ceramic profiles. A case study from central Italy

    Open Access•L Cardarelli•Journal of Archaeological Science•2022

  • Laser-Aided Profile Measurement and Cluster Analysis of Ceramic Shapes

    Open Access•Peter Demján, Peter Pavúk et al.•Journal of Field Archaeology•2023

  • An AI-assisted workflow for object detection and data collection from archaeological catalogues

    Open Access•Kevin Klein, Antoine Muller et al.•Journal of Archaeological Science•2025

  • Automatic monitoring of the bio colonisation of historical building's facades through convolutional neural networks (CNN)

    Open Access•Marco D''Orazio, Andrea Gianangeli et al.•Journal of Cultural Heritage•2024

  • Attention-enhanced U-Net for automatic crack detection in ancient murals using optical pulsed thermography

    Open Access•Jingwen Cui, Ning Tao et al.•Journal of Cultural Heritage•2024

  • Learning feature representation of Iberian ceramics with automatic classification models

    Open Access•Pablo Navarro, Celia Cintas et al.•Journal of Cultural Heritage•2021

  • The Nearest-Neighbor Statistic

    Open Access•David Pinder, Izumi Shimada et al.•American Antiquity•1979

Velocidad de citaciónhistorical
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Ethnos_APP • Proyecto Open Source • Licencia MIT • Frontend v2.0.0 • Privacidad y Cookies • Documentación de la API: api.ethnos.app/docs • Código de la API: GitHub • DOI: 10.5281/zenodo.17049435 • Código del Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae