ArchAIDE-Archaeological Automatic Interpretation and Documentation of cEramics

The goals of H2020 project "ArchAIDE: are to support the classification and interpretation work of archaeologists with innovative computer-based tools, able to provide the user with features for the semi-automatic description and matching of potsherds over the huge existing ceramic catalogues. Pottery classification is of fundamental importance for the comprehension and dating of the archaeological contexts, and for understanding production, trade flows and social interactions, but it requires complex skills and it is a very time consuming activity, both for researchers and professionals. The aim of ArchAIDE is to support the work of archaeologists, in order to meet real user needs and generate economic benefits, reducing time and costs. This would create societal benefits from cultural heritage, improving access, re-use and exploitation of the digital cultural heritage in a sustainable way. These objectives will be achieved through the development of: - an as-automatic-as-possible procedure to transform the paper catalogues in a digital description, to be used as a data pool for search and retrieval process; - a tool (mainly designed for mobile devices) that will support archaeologists in recognizing and classifying potsherds during excavation and post-excavation analysis, through an easy-to-use interface and efficient algorithms for characterisation, search and retrieval of the visual/geometrical correspondences; - an automatic procedure to derive a complete potsherds identity card by transforming the data collected into a formatted electronic document, printable or visual; - a web-based real-time data visualisation to improve access to archaeological heritage and generate new understanding; - an open archive to allow the archival and re-use of archaeological data, transforming them into common heritage and permitting economic sustainability. Those tools will be tested and assessed on real-cases scenarios, paving the way to future exploitation

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

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

NAVIGATING A NEW DIGITAL INTERFACE: USING AUTOMATED IMAGE RECOGNITION TO IDENTIFY POTTERY IN THE ARCHAIDE PROJECT

Archaeological Automatic Interpretation and Documentation of cEramic (ArchAIDE) is a H2020 funded project (2016-2019) developing digital tools to support archaeologists in recognising and classifying pottery. ArchAIDE is not designed to replace the knowledge of pottery specialists, but seeks to complement by speeding time consuming tasks, provide support for non-specialists, help students learn more about pottery recognition, and aid in the collection of metadata needed to describe the pottery. ArchAIDE is developing a tablet-based mobile app which relies upon image recognition and uses deep learning to narrow and suggest possible matches. While ArchAIDE has been careful to design a tool that allows classification decisions to be made by users at key points in the recording workflow, the app uses digital tools and methods for a significant tasks that were previously carried out using analogue methods. This paper will explore how users are engaging differently with the archaeology when using a digital workflow for identifying, classifying and recording pottery, as observed by the ArchAIDE project partners in early testing. This will include issues around using digitised comparative collections rather than paper catalogues, using the app to identify pottery while still in the field-rather than during post-excavation, how users might ‘see’ pottery differently through a digital rather than analogue analysis, and whether pottery identification using a digital interface changes knowledge transmission and learning processes. While the purpose of the ArchAIDE project is to make pottery identification faster and easier, this paper will pause to reflect and critically engage with moving to a digital workflow, and how this may influence how archaeological knowledge is produced and understood

The computerization of archaeology: survey on AI techniques

This paper analyses the application of artificial intelligence techniques to various areas of archaeology and more specifically: a) The use of software tools as a creative stimulus for the organization of exhibitions; the use of humanoid robots and holographic displays as guides that interact and involve museum visitors; b) The analysis of methods for the classification of fragments found in archaeological excavations and for the reconstruction of ceramics, with the recomposition of the parts of text missing from historical documents and epigraphs; c) The cataloguing and study of human remains to understand the social and historical context of belonging with the demonstration of the effectiveness of the AI techniques used; d) The detection of particularly difficult terrestrial archaeological sites with the analysis of the architectures of the Artificial Neural Networks most suitable for solving the problems presented by the site; the design of a study for the exploration of marine archaeological sites, located at depths that cannot be reached by man, through the construction of a freely explorable 3D version

How to access ancient landscapes? Field survey and legacy data integration for research on Greek and Roman settlement patterns in Eastern Sicily

The integration of field survey data from Eastern Sicily (the Plain of Catania) with legacy data avai- lable for the region will expand our knowledge on Mediterranean ancient rural landscapes. With an extent of 430 km2, the area is a perfect case study due to its geographical unity and the number of archaeological projects (both excavations and surveys) carried out within it in recent decades. Indeed, combining data from earlier research projects with new archaeological survey data allows us to conduct a settlement pattern analysis of the project’s study area. Heterogenous datasets have been integrated throu- gh their implementation into a geo-database, featuring the management and integration of topographical units and archaeological entities through semantic relations. Through geospatial data analysis based on the complete gazetteer of archaeological sites (whether sherd scatters, ruins, caves dwellings, tombs or tracks), a new image of rural landscapes for this area of Eastern Sicily from the Greek Archaic to the Late Roman Age can be visualized, beyond the traditional Sicilia frumentaria narrative

Study, revalorization and virtual musealization of a ceramic kiln based on information gathered from old excavations

[ES] Las posibilidades actuales de visualización y difusión a través de las tecnologías digitales tienen un efecto favorable en la conservación y la puesta en valor de los restos arqueológicos depositados en los museos. Por lo tanto, deben ser consideradas como herramientas esenciales en la gestión de las colecciones y una manera de comunicarse con todo tipo de usuarios, desde los que cuentan con un elevado perfil tecnológico hasta los visitantes ocasionales. El artículo presenta un caso de estudio en el cual se ha revisado la información recogida durante una serie de excavaciones arqueológicas relativas a los restos de un horno, las cuales se realizaron en la localidad de Orduña (España) en los años 2000 y 2001. Esta información, conjuntamente con una nueva inspección de las piezas almacenadas en el Museo Arqueológico de Bizkaia, ha permitido la generación de nuevos productos -como el modelo virtual tridimensional- que ofrecen posibilidades mejoradas de estudio, comprensión y difusión de las piezas, su origen y la importancia que el oficio de la cerámica y su comercio tuvieron en el pasado.[EN] The current possibilities of virtualization and dissemination by means of digital technologies have a favourable effect on the conservation and valorization of archaeological findings held in museums. Therefore, they should be considered as essential tools in the management of the collections and a way to communicate with all kind of users, from the ones with a highly technical profile to the occasional visitors. This article presents a case in point, in which the reviewing of the information generated during a series of archaeological excavations into the remains of a kiln, conducted in the town of Orduña (Spain) in 2000 and 2001, together with a new inspection of the pieces stored in the Bizkaia Museum of Archaeology, allowed for the generation of new products such as three-dimensional virtual models that improve the possibilities of studying, understanding and disseminating the pieces, their provenance and the importance that the craft and the trade of the pottery had in the past

A human–AI collaboration workflow for archaeological sites detection

This paper illustrates the results obtained by using pre-trained semantic segmentation deep learning models for the detection of archaeological sites within the Mesopotamian floodplains environment. The models were fine-tuned using openly available satellite imagery and vector shapes coming from a large corpus of annotations (i.e., surveyed sites). A randomized test showed that the best model reaches a detection accuracy in the neighborhood of 80%. Integrating domain expertise was crucial to define how to build the dataset and how to evaluate the predictions, since defining if a proposed mask counts as a prediction is very subjective. Furthermore, even an inaccurate prediction can be useful when put into context and interpreted by a trained archaeologist. Coming from these considerations we close the paper with a vision for a Human–AI collaboration workflow. Starting with an annotated dataset that is refined by the human expert we obtain a model whose predictions can either be combined to create a heatmap, to be overlaid on satellite and/or aerial imagery, or alternatively can be vectorized to make further analysis in a GIS software easier and automatic. In turn, the archaeologists can analyze the predictions, organize their onsite surveys, and refine the dataset with new, corrected, annotations