This work presents a semi-automatic approach to the 3D reconstruction of Heritage-Building Information Models from point clouds based on machine learning techniques. The use of digital information systems leveraging on three-dimensional (3D) representations in architectural heritage documentation and analysis is ever increasing. For the creation of such repositories, reality-based surveying techniques, such as photogrammetry and laser scanning, allow the fast collection of reliable digital replicas of the study objects in the form of point clouds. Besides, their output is raw and unstructured, and the transition to intelligible and semantic 3D representations is still a scarcely automated and time-consuming process requiring considerable human intervention. More refined methods for 3D data interpretation of heritage point clouds are therefore sought after. In tackling these issues, the proposed approach relies on (i) the application of machine learning techniques to semantically label 3D heritage data by identification of relevant geometric, radiometric and intensity features, and (ii) the use of the annotated data to streamline the construction of Heritage-Building Information Modeling (H-BIM) systems, where purely geometric information derived from surveying is associated with semantic descriptors on heritage documentation and management. The “Grand-Ducal Cloister” dataset, related to the emblematic case study of the Pisa Charterhouse, is discussed.

Abstract. Research in the field of Cultural Heritage is increasingly moving towards the creation of digital information systems, in which the geometric representation of an artifact is linked to some external information, through meaningful tags. The process of attributing additional and structured information to various elements in a given digital model is customarily identified with the term semantic annotation; the added contextual information is associated, for instance, to analysis and conservation terms. Starting from the existing literature, aim of this work is to discuss how semantic annotations are used, in digital architectural heritage models, to link the geometrical representation of an artefact with knowledge-related information. Most consolidated methods -such as traditional mapping on 2D media, are compared with more recent approaches making the most of 3D representation. Reference is made, in particular, to Heritage-BIM techniques and to collaborative reality-based platforms, such as Aïoli (http://aioli.cloud). Potentialities and limits of the different solutions proposed in literature are critically discussed, also addressing future research challenges in Cultural Heritage application fields.

In an increasingly competitive industry, tourism managers are faced with the necessity of estimating future values of demand in the short term despite the limitations of scarcity, volatility and uncertainty. A convenient and flexible approach such as judgemental forecasting holds promise in addressing the major issues in the field of tourism demand forecasting. This paper presents an innovative approach, which uses the opportunities offered by decision support systems to tackle the main issues associated with judgemental forecasting. A forecasting system that supports collaborative short-term forecasting tasks among tourism managers is offered as a case example.

A solid understanding of when travel decisions are made in relation to travelers’ planning horizons is crucial for travel service providers. Despite its importance, there are very few empirical studies investigating the planning horizon and its antecedents in travel research literature. This study contributes to bridging this gap by conceptualizing a two-level model of antecedents of travelers’ planning horizons. In addition to individual traveler- and trip-related aspects, the model provides a cross-cultural perspective on international travelers’ planning horizons by including uncertainty avoidance, individualism, and long-term orientation as cultural-level antecedents. Drawing on a nested dataset of 4,074 international travelers from 17 countries worldwide, the results of a two-level hierarchical regression model show that, in addition to individual-level aspects, cultural antecedents play an important role in determining planning horizons. Based on the empirical results, the paper discusses implications for theory and travel service providers.