Many projects concerning the protection, conservation, restoration, and dissemination of cultural heritage are being carried out around the world due to its growing interest as a driving force of socio-economic development. The existence of reliable, digital three-dimensional (3D) models that allow for the planning and management of these projects in a remote and decentralized way is currently a growing necessity. There are many software tools to perform the modeling and complete three-dimensional documentation of the intervened monuments. However, the Architecture, Engineering and Construction (AEC) sector has adopted the Building Information Modeling (BIM) standard over the last few decades due to the progress that has been made in its qualities and capabilities. The complex modeling of cultural heritage through commercial BIM software leads to the consideration of the concept of Heritage BIM (H-BIM), which pursues the modeling of architectural elements, according to artistic, historical, and constructive typologies. In addition, H-BIM is considered to be an emerging technology that enables us to understand, document, advertize, and virtually reconstruct the built heritage. This article is a review of the existing literature on H-BIM and its effective implementation in the cultural heritage sector, exploring the effectiveness and the usefulness of the different methodologies that were developed to model families of elements of interest.

Abstract:The classification of the images taken during the measurement of an architectural asset is an essential task within the digital documentation of cultural heritage. A large number of images are usually handled, so their classification is a tedious task (and therefore prone to errors) and habitually consumes a lot of time. The availability of automatic techniques to facilitate these sorting tasks would improve an important part of the digital documentation process. In addition, a correct classification of the available images allows better management and more efficient searches through specific terms, thus helping in the tasks of studying and interpreting the heritage asset in question. The main objective of this article is the application of techniques based on deep learning for the classification of images of architectural heritage, specifically through the use of convolutional neural networks. For this, the utility of training these networks from scratch or only fine tuning pre-trained networks is evaluated. All this has been applied to classifying elements of interest in images of buildings with architectural heritage value. As no datasets of this type, suitable for network training, have been located, a new dataset has been created and made available to the public. Promising results have been obtained in terms of accuracy and it is considered that the application of these techniques can contribute significantly to the digital documentation of architectural heritage.

In this paper, a new approach for the virtual modeling and reconstruction of Architectural Heritage is presented. The graphic and semantic information required to determine the conservation status of the analyzed buildings, obtained from point clouds and historical and bibliographical data, are combined. The modeled components are used to create a library of parametric elements under the concept of Heritage Building Information Modeling (HBIM). This represents a solution for the 3D modeling of a wide range of buildings in the same style, due to the flexibility of the modeled elements which can change in shape and proportions, thus adapting to new requirements. Moreover, technical documentation and quantitative and qualitative information can be produced, allowing detailed analysis in a remote and multidisciplinary way within the general framework of "Smart heritage".

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Moisture is one of the main factors in the deterioration of heritage buildings, causing mould, unwanted parasites, and the decanting of salts, which, in turn, aggravate such degradation. The existence of moisture not only affects the building aesthetically, but is also evidence of bad conservation conditions. It is thus extremely important to verify and assess the extent of the moisture, even though it may not be confirmed by mere visual inspection. This article describes an innovative and straightforward procedure to automatically show where moisture appears, as well as the affected area. The procedure is based on the use of 3D laser scanner surveying data for documentation purposes of historic buildings. Data are processed off-line in order to analyse the laser reflectivity level. The method is not intrusive, allows large areas to be covered in a short time, and does not interact with the materials, which makes it optimal for application to these special buildings. Hence, professionals of the conservation sector will have objective and comprehensive information on moisture damage, helping them to take decisions on the action to be undertaken. The results achieved in the Cathedral of Ciudad Rodrigo (Spain) are shown to demonstrate the utility of the proposed method.Keywords: Moisture detection, Multi-point humidity measurement, Moisture optical measurement systems, 3D laser scanning, Index of reflectivity, Surface reflectivity measurement Research objectiveThe present work is aimed at defining a novel, practical method for obtaining 3D digital models that clearly show where moisture appears, and also where it is likely to appear, in heritage buildings. Alternative methods in use are improved, and decision making on the corrective and preventive conservation measures to be adopted by cultural heritage owners or managing organizations will be facilitated. In addition, the competitiveness of related conservation and restoration enterprises will be encouraged.To this end, an applied research approach is proposed, which combines state-of-the-art 3D data acquisition, with a tailored computational algorithm for managing the reflectivity index provided by laser scanning devices. Hence, a useful unique digital model, including geometrical, colour, and reflectivity information of complex shaped objects, could be readily obtained, thereby favouring not only the cataloguing, but also revealing the moisture content and extension of the original sites.Time, expenditure, and areas to be cleared up can be clearly defined by the proposed method, which has been implemented through a practical tool for handling 3D point clouds, giving support to current and further automatic procedures.

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