AUTOMATIC IDENTIFICATION OF ARCHAEOLOGICAL ARTIFACTS ON THE EXCAVATION SITE

Abstract. Archaeological data is processed to ensure that it can be easily accessed and used. The integration of the documentation into GIS tools is carried out in the post-excavation phase. The final documents are completed on the basis of intermediate documents made on the excavation site. Time during the excavation is precious and any action that takes time is questioned to allow to devote a maximum of resources to the most important tasks. Many tasks are associated with a traditional paper entry. The aim of this study is to experiment with the use of means of automating the management of archaeological documents in order to minimize the repetition of recording acts of different kind. The integration of computer technology in the field is gradually being achieved through the use of tablets, but their use on the excavation site remains a strong constraint. The first task of this automation lies in the possibility of identifying objects of interest during the excavation. In order to make this recognition of archaeological entities possible it is necessary to ask when they are easily identifiable: in the excavation report. The hypothesis formulated here is that excavation reports can be used as a source for creating learning data sets of neural networks dedicated to the recognition of archaeological objects on site. Two important steps in automating the integration of archaeological data are presented here, the extraction of images and their semantics from excavation reports and the learning process of a neural network for the recognition of archaeological entities at the site of their discovery. The extraction of images and the identification of what they contain allows to enrich neural network learning datasets. Tests have been made to validate the ability of such tools to reliably identify particular objects. We chose CNN to test the ability to recognize archaeological objects in an excavation context. It is an image-based network. What is sought here is the ability to recognize an object for a neural network.