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.

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.

As the basis for analysis and management of heritage assets, 3D laser scanning and photogrammetric 3D reconstruction have been probed as adequate techniques for point cloud data acquisition. The European Directive 2014/24/EU imposes BIM Level 2 for government centrally procured projects as a collaborative process of producing federated discipline-specific models. Although BIM software resources are intensified and increasingly growing, distinct specifications for heritage (H-BIM) are essential to driving particular processes and tools to efficiency shifting from point clouds to meaningful information ready to be exchanged using non-proprietary formats, such as Industry Foundation Classes (IFC). This paper details a procedure for processing enriched 3D point clouds into the REVIT software package due to its worldwide popularity and how closely it integrates with the BIM concept. The procedure will be additionally supported by a tailored plug-in to make high-quality 3D digital survey datasets usable together with 2D imaging, enhancing the capability to depict contextualized important graphical data to properly planning conservation actions. As a practical example, a 2D/3D enhanced combination is worked to accurately include into a BIM project, the length, orientation, and width of a big crack on the walls of the Castle of Torrelobatón (Spain) as a representative heritage building.

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.

Self-inspection of buildings is the process of controlling the quality of construction work in order to ensure that the specifications are implemented according to the design. Under current practices, self-inspection is totally dependent on the operator’s skills, being a process that can be time-consuming and sometimes difficult to achieve. The Intuitive Self-Inspection Techniques using Augmented Reality (INSITER; for construction, refurbishment and maintenance of energy-efficient buildings made of prefabricated components) project aims to develop a software tool to support construction workers in self-inspection processes, with the overall objective of reducing major errors and extra costs. Nevertheless, one of the challenges is the lack of interoperability between the various equipment used to carry out self-inspection. Devices and current tools deployed on-site do not speak the same language, which leads to a lack of communication. Therefore, this paper presents a framework under which the equipment would be able to send information in a common format. For this purpose, the Industry Foundation Classes (IFC) de-facto standard has been established as a viable data model to represent all the information related to the building project. Along these lines, Building Information Modeling (BIM) information and IFC-compliant databases have been designed for the representation of data coming from Computer-Aided Design (CAD) modeling, laser scanning, thermography and sensor networks. Besides the IFC-data repositories, the framework is a multi-layer architecture with the goal of ensuring interoperability and promoting the stakeholders’ objectives for self-inspection during the entire construction process.