According to the 2017 International Council of Museums (ICOM) guidelines, data on museum col-lections must be stored in a secure environment, supported by backup systems that allow access by all legitimate users, complete and unique identification, and description (associations, provenance, condition, treatment and current location) of each object are required.Concerning these indications, it is therefore, a priority to establish precise protocols for the preven-tive conservation and analysis of data concerning not only the identity of the asset or the information collected during its study, but also how it is preserved.This paper proposes a digital framework for the management of museum structures and collections, integrating Building Information Modelling (BIM) methodologies for the preservation and visualization of data with Internet of Things (IoT) methodologies for its collection and analysis.
Real-Time Identification of Artifacts:Synthetic Data for AI Model
The collections represent the constitutive element and the raison d’être of each museum. Their man-agement, care and dissemination are therefore a task of primary importance for every museum. Applying new Artificial Intelligence technologies in this area could lead to new initiatives. However, the development of certain tools requires structured and labeled datasets for the training phases which are not always easily available. The proposed contribution is within the domain of the construction of specific datasets with low budget tools and explores the results of a first step in this direction by testing algorithms for the recognition and labeling of heritage objects. The developed workflow is part of a first prototype that could be used both in heritage dissemination or gamification applications, and for use in heritage research tools.
Photogrammetric Survey for a Fast Construction of Synthetic Dataset
In this work we show how Physically Based Rendering (PBR) tools can be used to extend the training image datasets of Machine Learning (ML) algorithms for the recognition of built heritage. In the field of heritage valorization, the combination of Artificial Intelligence (AI) and Augmented Reality (AR) has allowed to recognize built heritage elements with mobile devices, anchoring digital products to the physical environment in real time, thus making the access to information related to real space more intuitive and effective. However, the availability of training data required for these systems is extremely limited and a large–scale image dataset is required to achieve accurate results in image recognition. Manually collecting and annotating images can be very resource and time–consuming. In this contribution we explore the use of PBR tools as a viable alternative to supplement an otherwise inadequate dataset.