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Technology and the fruition of cultural heritage are becoming increasingly more entwined, especially with the advent of smart audio guides, virtual and augmented reality, and interactive installations. Machine learning and computer vision are important components of this ongoing integration, enabling new interaction modalities between user and museum. Nonetheless, the most frequent way of interacting with paintings and statues still remains taking pictures. Yet images alone can only convey the aesthetics of the artwork, lacking is information which is often required to fully understand and appreciate it. Usually this additional knowledge comes both from the artwork itself (and therefore the image depicting it) and from an external source of knowledge, such as an information sheet. While the former can be inferred by computer vision algorithms, the latter needs more structured data to pair visual content with relevant information. Regardless of its source, this information still must be be effectively transmitted to the user. A popular emerging trend in computer vision is Visual Question Answering (VQA), in which users can interact with a neural network by posing questions in natural language and receiving answers about the visual content. We believe that this will be the evolution of smart audio guides for museum visits and simple image browsing on personal smartphones. This will turn the classic audio guide into a smart personal instructor with which the visitor can interact by asking for explanations focused on specific interests. The advantages are twofold: on the one hand the cognitive burden of the visitor will decrease, limiting the flow of information to what the user actually wants to hear; and on the other hand it proposes the most natural way of interacting with a guide, favoring engagement. Comment: accepted at FlorenceHeritech 2020

The Mausoleum of Galla Placidia is one of Ravenna’s UNESCO protected monuments, globally renowned for the extraordinary mosaic decorations that cover the internal surfaces. The famous starry vault profoundly engages and inspires the observer. It has been studied for its accuracy in the representation of the real sky, but also because of its mystical and symbolic meaning in relation to the iconographic tradition of the time. The building has also been subject of archeoastronomical research (Romano in Orientamenti ad sidera. Astronomia, riti e calendari per la fondazione di templi e citta. Un esempio a Ravenna. Edizioni Essegi, Ravenna, 1995), which is here presented in depth. The present contribution also examines other architectural elements beyond orientation: particular attention is payed to the small slit windows of the building to investigate their possible archaeoastronomical significance. In the study of these elements, particular attention should be payed to the elaboration of architectural survey data, which has to be produced following established procedures and techniques. A functional 3D model will be developed from the data of the archaeoastronomical analysis to display the original morphology of the building (the floor was about 1.4 m lower because of subsidence movements), astronomical phenomena, and allow for multimedia communication of the scientific content produced. Finally, the related issues will be investigated: the geometric and projective transformations of the starry dome, the geometric shape of space also in relation to the unit of measurement used.

The paper presents the first results of the Cortona Heritage Project, which takes the opportunity to use the techniques of Virtual heritage to implementing concrete and innovative modes making the very rich cultural heritage of ancient Tuscan cities accessible for more people and engaging new publics by promoting its knowledge among young generations.

Abstract Research activity on ejectors is ongoing at the University of Florence since the late nineties. The most important achievement is a 40 kW ejector chiller designed according to the “CRMC” criterion. The experimentally validated CFD simulations have given some hints about some possible improvements, i.e. refine the surface finish of the ejector, study the effect of heat transfer and improve the final part of the diffuser, which in its present shape does not produce a measurable compression. The prototype has been recently filled with low-GWP refrigerant R1233zd, as a drop-in replacement of previously used R245fa. Both fluids are “dry-expanding” and hence significantly easier to model in CFD simulations. Synthetic low-GWP refrigerants may be an option for ejector chillers, due to their ability to reach below-zero temperature and high volumetric refrigerant capacity. Some lessons learned with synthetic refrigerants can be transferred to the project of a steam ejector chiller, which remains one of our future targets. Herein we resume the principal findings gathered by means of experimental and numerical activity on our prototype and propose a few ideas for the future research.

Abstract. Cultural heritage digitization and 3D modelling processes are mainly based on laser scanning and digital photogrammetry techniques to produce complete, detailed and photorealistic three-dimensional surveys: geometric as well as chromatic aspects, in turn testimony of materials, work techniques, state of preservation, etc., are documented using digitization processes. The paper explores the topic of 3D documentation for conservation purposes; it analyses how geomatics contributes in different steps of a restoration process and it presents an overview of different uses of 3D models for the conservation and enhancement of the cultural heritage. The paper reports on the project to digitize the earthenware frieze of the Ospedale del Ceppo in Pistoia (Italy) for 3D documentation, restoration work support, and digital and physical reconstruction and integration purposes. The intent to design an exhibition area suggests new ways to take advantage of 3D data originally acquired for documentation and scientific purposes.

This paper discusses an approach for identification of historic buildings that combines Terrestrial Laser Scanning (TLS) survey, Deviation Analysis (DA) and Finite Element (FE) numerical modelling. The methodology is presented through the application to an illustrative case study: an early medieval period brick minaret located in Aksaray (Turkey). Precise direction of inclination, leaning angle, local deviations from circular building shape, deflections from vertical planes, local curvatures and related maps were obtained with high accuracy by DA, based on detailed point cloud 3D mesh model. In addition, differently from traditional approaches in FE analysis, a method for direct transfer of high accuracy TLS based 3D model to FE structural analysis is introduced. The FE model is subsequently employed to interpret and verify structural health of the historic building. Conference on Florence Heri-Tech - The Future of Heritage Science and Technologies -- MAY 16-18, 2018 -- Florence, ITALY WOS: 000452025100085 Univ Florence

The engineering of software product lines begins with the identification of the possible variation points. To this aim, natural language (NL) requirement documents can be used as a source from which variability-relevant information can be elicited. In this paper, we propose to identify variability issues as a subset of the ambiguity defects found in NL requirement documents. To validate the proposal, we single out ambiguities using an available NL analysis tool, QuARS, and we classify the ambiguities returned by the tool by distinguishing among false positives, real ambiguities, and variation points, by independent analysis and successive agreement phase. We consider three different sets of requirements and collect the data that come from the analysis performed.

The most time-honoured tool for understanding the processes of the human past is represented by archaeological excavation. By examining an area at discrete temporal periods, archaeologists are literally able to look backwards in time: they can analyse incomplete material records in order to understand and reconstruct the cultural history of an area at particular moments in time. Since the digging process destroys the site forever, great care must be paid during both the excavation and the documentation. In general, after a stratum has been completely excavated, both the floors and walls are cleaned and made ready for documentation. Photos of both the sides and bedrock of a given excavation are collected, and several sketches of what the archaeologists have seen in the trenches are made. In these drawings are delineated the features and shapes of artefacts on the horizontal plane. In addition, depending on the colours and similarities of the textures, drawing are also made of the archaeological layers. This approach is time-consuming, is affected by human ability, and does not make possible a prompt digitization of the results. Within this context, the automatized identification of archaeological stratigraphy during excavation work is welcomed by archaeologists. Here, a k-means unsupervised machine learning algorithm has been used for colour clustering digital images of excavation sites. The algorithm that we have developed attempts to enhance the colour similarity while keeping the colours separate one from another as much as possible. The main idea is that pixels belonging to the same colour cluster are a part of the same layer. Once the layer has been identified, a statistical approach based on Haralick features is used to characterize each strata in terms of texture. Unsupervised machine learning combined with texture analysis could become a good practice in speeding up the documentation work of archaeologists and paving the way towards the creation of an "automated archaeologist".

The interest in high-resolution semantic 3D models of historical buildings continuously increased during the last decade, thanks to their utility in protection, conservation and restoration of cultural heritage sites. The current generation of surveying tools allows the quick collection of large and detailed amount of data: such data ensure accurate spatial representations of the buildings, but their employment in the creation of informative semantic 3D models is still a challenging task, and it currently still requires manual time-consuming intervention by expert operators. Hence, increasing the level of automation, for instance developing an automatic semantic segmentation procedure enabling machine scene understanding and comprehension, can represent a dramatic improvement in the overall processing procedure. In accordance with this observation, this paper aims at presenting a new workflow for the automatic semantic segmentation of 3D point clouds based on a multi-view approach. Two steps compose this workflow: first, neural network-based semantic segmentation is performed on building images. Then, image labelling is back-projected, through the use of masked images, on the 3D space by exploiting photogrammetry and dense image matching principles. The obtained results are quite promising, with a good performance in the image segmentation, and a remarkable potential in the 3D reconstruction procedure. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVI-2/W1-2022 ISSN:2194-9034 ISSN:1682-1777 ISSN:1682-1750

Here we address the roadmap of the Digital Cultural Heritage research group DigitCH group, which was set up in 2013 at the Department of Architecture, University of Florence. The aim of DigitCH group was to realize the link between scientifically validated methodologies and contents, innovative storytelling, and technological instrumentation. The spread of electronic devices has enabled rapid and easy technological fallout of research in the field of the acquisition-representation of the survey data expanding audiences and accelerating even an innovative approach to the whole knowledge of CH.