Dealing with coloured point cloud acquired from terrestrial laser scanner, this paper identifies remaining challenges for a new data structure: the <i>smart point cloud</i>. This concept arises with the statement that massive and discretized spatial information from active remote sensing technology is often underused due to data mining limitations. The generalisation of point cloud data associated with the heterogeneity and temporality of such datasets is the main issue regarding structure, segmentation, classification, and interaction for an immediate understanding. We propose to use both point cloud properties and human knowledge through machine learning to rapidly extract pertinent information, using user-centered information (smart data) rather than raw data. A review of feature detection, machine learning frameworks and database systems indexed both for mining queries and data visualisation is studied. Based on existing approaches, we propose a new 3-block flexible framework around device expertise, analytic expertise and domain base reflexion. This contribution serves as the first step for the realisation of a comprehensive smart point cloud data structure.

Preventive strategies for the conservation of heritage sites have gradually been preferred to curative approaches because of their ability to maintain their significance. Furthermore, most experts now agree that conservation management of heritage places based on a common understanding of their cultural values is essential to address all the particularities of their contexts. Recently, significant research has demonstrated the potential of Heritage Building Information Modelling (HBIM) for the collaborative data management in conjunction with conservation projects. The recent development of HBIM web platforms illustrates the value of strengthening the link between the digital model and the physical realm of heritage assets. This paper advocates the application of Digital Twin’s (DT) principles, using HBIM models as a digital replica, to support the preventive conservation of heritage places. Based on an extensive literature review, a comprehensive framework that integrates the DT into the management plan process for the preventive conservation of built heritage is proposed. Several recommendations for its implementation are finally discussed, such as the identification of tangible features of significance, the threats associated with their integrity and the corresponding mitigation strategies, with particular emphasis on the value assessment process. The result is a data model for structuring information on preventive conservation strategies. This framework provides the basis for future implementation and demonstrates the need for a DT approach in this context.

<p><strong>Abstract.</strong> During preliminary phases of conservation projects, a considerable amount of heterogeneous datasets are produced, gathered, analysed and interpreted. Abundant researches have gradually proven that Historic Building Information Modelling (HBIM) is a relevant alternative for the collaborative management of information related to existing structures. Apart from the obvious benefits of HBIM for information exchange among stakeholders during conservation project, the potential of such processes to support preservation strategies should not be neglected. Moreover, the recent developments of HBIM web-interfaces illustrate the need for additional investigation in strengthening the relationships between the digital model and the real-world to better support preventive conservation of heritage places. Besides, values-based approaches for the elaboration of conservation strategies have been gradually adopted in the last decades, both in academic and professional sector. In this paper, we propose a comprehensive methodology to structure and integrate the cultural significance of tangible and intangible elements into HBIM models to be further taken into account in the analysis and simulation of data. This article suggests the application of Digital Twin (DT) principles to support site managers in the preventive conservation of their assets. Based on the analysis and simulations of data captured by onsite sensors, threats to the site integrity and corresponding preventive solution can be predicted in the DT environment. The integration and structuration of Heritage Values in HBIM models allow further evaluation process to estimate the impact of each suggested interventions on the conservation of features of significance.</p>

ABSTRACT:This paper proposes an interoperable model for managing high dimensional point clouds while integrating semantics. Point clouds from sensors are a direct source of information physically describing a 3D state of the recorded environment. As such, they are an exhaustive representation of the real world at every scale: 3D reality-based spatial data. Their generation is increasingly fast but processing routines and data models lack of knowledge to reason from information extraction rather than interpretation. The enhanced smart point cloud developed model allows to bring intelligence to point clouds via 3 connected meta-models while linking available knowledge and classification procedures that permits semantic injection. Interoperability drives the model adaptation to potentially many applications through specialized domain ontologies. A first prototype is implemented in Python and PostgreSQL database and allows to combine semantic and spatial concepts for basic hybrid queries on different point clouds.

ABSTRACT:Virtual Leodium is an interdisciplinary project aiming to develop an archaeological information system based on a city scale model. The first part of the paper describes current project's achievements; the general methodology and the workflow of the project, namely the production and the modelling of archaeological data; the prototype functions of the ad hoc developed archaeological information system. The second part of the paper presents our new Virtual Leodium archaeological information modelling approach, which aims at consider, in a more comprehensive way, the complexity of archaeological information.