, Marco Callieri4, Nachum Dershowitz5, Nevio Dubbini6, Diego Lucendo Diaz7, Tim Evans8, Gabriele Gattiglia9, Katie Green10, Maria Letizia Gualandi11, Miguel Angel Hervas12, Barak Itkin13, Marisol Madrid i Fernandez14, Eva Miguel Gascón15, Michael Remmy16, Julian Richards17, Roberto Scopigno18, Llorenç Vila19, Lior Wolf20, Holly Wright21, Massimo Zallocco22Cite this as: Anichini, F. et al. 2020 Developing the ArchAIDE Application: A digital workflow for identifying, organising and sharing archaeological pottery using automated image recognition, Internet Archaeology 52. https://doi.org/10.11141/ia.52.7
Overiew of the ArchAIDE workflow (60 seconds). Taken from ArchAIDE consortium (2019)
Pottery is of fundamental importance for understanding archaeological contexts, facilitating the understanding of production, trade flows, and social interactions. Pottery characterisation and the classification of ceramics is still a manual process, reliant on analogue catalogues created by specialists, held in archives and libraries. The ArchAIDE project worked to streamline, optimise and economise the mundane aspects of these processes, using the latest automatic image recognition technology, while retaining key decision points necessary to create trusted results. Specifically, ArchAIDE worked to support classification and interpretation work (during both fieldwork and post-excavation analysis) with an innovative app for tablets and smartphones. This article summarises the work of this three-year project, funded by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement N.693548, with a consortium of partners representing both the academic and industry-led ICT (Information and Communications Technology) domains, and the academic and development-led archaeology domains. The collaborative work of the archaeological and technical partners created a pipeline where potsherds are photographed, their characteristics compared against a trained neural network, and the results returned with suggested matches from a comparative collection with typical pottery types and characteristics. Once the correct type is identified, all relevant information for that type is linked to the new sherd and stored within a database that can be shared online. ArchAIDE integrated a variety of novel and best-practice approaches, both in the creation of the app, and the communication of the project to a range of stakeholders.
Go to article Table of Contents.
1. Università di Pisa. Mappa Lab
2. Consiglio Nazionale delle Ricerche
CNR-ISTI Visual Computing Lab (VCL)
3. Universitat de Barcelona,
ARQUB Material Culture and Archaeometry
4. Consiglio Nazionale delle Ricerche,
CNR-ISTI Visual Computing Lab (VCL)
5. Tel Aviv University,
School of Computer Science
6. University of Pisa
7.
BARAKA
8. University of York,
Archaeology Data Service (ADS)
9. Università di Pisa,
Mappa Lab
10. University of York,
Archaeology Data Service (ADS)
11. Università di Pisa,
Mappa Lab
12.
BARAKA
13. Tel Aviv University,
School of Computer Science
14. Universitat de Barcelona,
ARQUB Material Culture and Archaeometry
15. Universitat de Barcelona,
ARQUB Material Culture and Archaeometry
16. Universität zu Köln,
CoDArchLab - Archaeological Institute
17. University of York,
Archaeology Data Service (ADS)
18. Consiglio Nazionale delle Ricerche,
CNR-ISTI Visual Computing Lab (VCL)
19. Elements
20. Tel Aviv University,
School of Computer Science
21. *Corresponding author: University of York, Archaeology Data Service (ADS). Email: holly.wright@york.ac.uk
22. Inera