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Remote Sensing (RS) and Geographic Information Science (GIS) techniques are powerful tools for spatial data collection, analysis, management, and digitization within cultural heritage frameworks. Despite their capabilities, challenges remain in automating data semantic classification for conservation purposes. To address this, leveraging airborne Light Detection And Ranging (LiDAR) point clouds, complex spatial analyses, and automated data structuring is crucial for supporting heritage preservation and knowledge processes. In this context, the present contribution investigates the latest Artificial Intelligence (AI) technologies for automating existing LiDAR data structuring, focusing on the case study of Sardinia coastlines. Moreover, the study preliminary addresses automation challenges in the perspective of historical defensive landscapes mapping. Since historical defensive architectures and landscapes are characterized by several challenging complexities—including their association with dark periods in recent history and chronological stratification—their digitization and preservation are highly multidisciplinary issues. This research aims to improve data structuring automation in these large heritage contexts with a multiscale approach by applying Machine Learning (ML) techniques to low-scale 3D Airborne Laser Scanning (ALS) point clouds. The study thus develops a predictive Deep Learning Model (DLM) for the semantic segmentation of sparse point clouds (2), adaptable to large landscape heritage contexts and heterogeneous data scales. Additionally, a preliminary investigation into object-detection methods has been conducted to map specific fortification artifacts efficiently.

In the city of Valencia (Spain), there existed from the Middle Ages until the mid-nineteenth century a building that fulfilled a municipal strategic function: The “new flour-weighing house”. Its purpose was to distribute food to the population and collect the corresponding indirect municipal taxes. Today, the existence of this building is not remembered, neither by scientists nor by citizens, and its importance, location and appearance are unknown. The building investigated, behind which the medieval façade of the “flour-weighing house” is hidden, is the Colomina Palace. In the investigation, its growth phases have been detected, and an idea of its structural organisation has been obtained. Research and investigation have been carried out by consulting historical, cartographic and archival material, together with advanced geomatics techniques, including close-range photogrammetry, terrestrial laser scanning and thermography. The fuse of colour and thermal imagery, together with point clouds and 3D models, help to visualise and check the different spatial transformations of the current “Colomina Palace”, adapting the sequence from medieval times into present. The methodology proposed in this study avoids the need to carry out destructive tests and the processing of permits, which speeds up decision-making and historical architectural reconstruction.

The interest in the enhancement of innovative solutions in the geospatial data classification domain from integrated aerial methods is rapidly growing. The transition from unstructured to structured information is essential to set up and arrange geodatabases and cognitive systems such as digital twins capable of monitoring territorial, urban, and general conditions of natural and/or anthropized space, predicting future developments, and considering risk prevention. This research is based on the study of classification methods and the consequent segmentation of low-altitude airborne LiDAR data in highly forested areas. In particular, the proposed approaches investigate integrating unsupervised classification methods and supervised Neural Network strategies, starting from unstructured point-based data formats. Furthermore, the research adopts Machine Learning classification methods for geo-morphological analyses derived from DTM datasets. This paper also discusses the results from a comparative perspective, suggesting possible generalization capabilities concerning the case study investigated.

Digitisation processes of movable heritage are becoming increasingly popular to document the artworks stored in our museums. A growing number of strategies for the three-dimensional (3D) acquisition and modelling of these invaluable assets have been developed in the last few years. Their objective is to efficiently respond to this documentation need and contribute to deepening the knowledge of the masterpieces investigated constantly by researchers operating in many fieldworks. Nowadays, one of the most effective solutions is represented by the development of image-based techniques, usually connected to a Structure-from-Motion (SfM) photogrammetric approach. However, while images acquisition is relatively rapid, the processes connected to data processing are very time-consuming and require the operator’s substantial manual involvement. Developing deep learning-based strategies can be an effective solution to enhance the automatism level. In this research, which has been carried out in the framework of the digitisation of a wooden maquettes collection stored in the ‘Museo Egizio di Torino’, using a photogrammetric approach, an automatic masking strategy using deep learning techniques is proposed, to increase the level of automatism and therefore, optimise the photogrammetric pipeline. Starting from a manually annotated dataset, a neural network was trained to automatically perform a semantic classification to isolate the maquettes from the background. The proposed methodology allowed the researchers to obtain automatically segmented masks with a high degree of accuracy. The workflow is described (as regards acquisition strategies, dataset processing, and neural network training). In addition, the accuracy of the results is evaluated and discussed. Finally, the researchers proposed the possibility of performing a multiclass segmentation on the digital images to recognise different object categories in the images, as well as to define a semantic hierarchy to perform automatic classification of different elements in the acquired images. Highlights: • In the framework of movable heritage digitisation processes, many procedures are very time-consuming, and they still require the operator’s substantial manual involvement. • This research proposes using deep learning techniques to enhance the automatism level in the generation of exclusion masks, improving the optimisation of the photogrammetric procedures. • Following this strategy, the possibility of performing a multiclass semantic segmentation (on the 2D images and, consequently, on the 3D point cloud) is also discussed, considering the accuracy of the obtainable results.

The architectural heritage of the 20th century is affected by several conservation problems in terms of material preservation, structural analysis, and reuse. Among these, material degradation and durability issues are the ones that have the most effect on the health state and, consequently, the survival of the constructions of the period. In order to conduct a proper analysis for preservation purposes, an interdisciplinary approach is necessary. The parabolic arch in Morano sul Po (Italy) is a reinforced concrete landmark in the Casale Monferrato area and is related to the industrial vocation of the territory, which is indissolubly linked to the cement production chain. The present paper reports the results of a non-destructive test campaign by a Politecnico di Torino multidisciplinary group, which combined acquisitions using different methods. The paper highlights the importance of a structured procedure to integrate different information coming from different techniques. The aim was to assess the health state of the structure and define the best procedures for building an information system based on the as-built modeling strategy, which could serve as the basis to provide conservation guidelines.

In the wide scenario of heritage documentation and conservation, the multi-scale nature of digital models is able to twin the real object, as well as to store information and record investigation results, in order to detect and analyse deformation and materials deterioration, especially from a structural point of view. The contribution proposes an integrated approach for the generation of an n-D enriched model, also called a digital twin, able to support the interdisciplinary investigation process conducted on the site and following the processing of the collected data. Particularly for 20th Century concrete heritage, an integrated approach is required in order to adapt the more consolidated approaches to a new conception of the spaces, where structure and architecture are often coincident. The research plans to present the documentation process for the halls of Torino Esposizioni (Turin, Italy), built in the mid-twentieth century and designed by Pier Luigi Nervi. The HBIM paradigm is explored and expanded in order to fulfil the multi-source data requirements and adapt the consolidated reverse modelling processes based on scan-to-BIM solutions. The most relevant contributions of the research reside in the study of the chances of using and adapting the characteristics of the IFC (Industry Foundation Classes) standard to the archiving needs of the diagnostic investigations results so that the digital twin model can meet the requirements of replicability in the context of the architectural heritage and interoperability with respect to the subsequent intervention phases envisaged by the conservation plan. Another crucial innovation is a proposal of a scan-to-BIM process improved by an automated approach performed by VPL (Visual Programming Languages) contribution. Finally, an online visualisation tool enables the HBIM cognitive system to be accessible and shareable by stakeholders involved in the general conservation process.

Historical documents represent a significant part of the world cultural heritage and need to be preserved from physical deformation due to ageing. The restoration of fragile documents requires economic resources that are often limited to only preserve the integrity of exceptional and highly valuable historical records. On the other hand, regeneration of ancient documents in digital form is a useful way to preserve them regardless of the material they are made of. In addition, the digitization of historical cartography allows creating a valuable dataset for a variety of GIS applications as well as spatial and landscape studies. Nonetheless, historical maps are usually deformed, and a contact-scanning process could damage them because this method requires planar positioning of the map. In this regard, photogrammetry has been used successfully as a non-invasive method to digitize historical documentation. The purpose of this research is to assess a low-cost and highly flexible strategy to digitize historical maps and documents through digital photogrammetry using low-cost commercial off-the-shelf sensors. This methodology allows training a wider audience of cultural heritage operators in digitizing historic records with a millimeter-level accuracy.

In the framework of built heritage monitoring techniques, a prominent position is occupied by thermography, which represents an efficient and non-invasive solution for these kinds of investigations, allowing the identification of phenomena detectable only in the non-visible range of the electromagnetic spectrum. This is of extreme interest, especially considering the possibility of integrating the radiometric information with the 3D models achievable from laser scanning or photogrammetric techniques, characterised by a high spatial resolution. This paper aims to illustrate how combining different geomatics techniques (in particular, by merging thermal images, laser scanning point clouds, and traditional visible colour photogrammetric data) can efficiently support historical analyses for studying heritage buildings. Additionally, a strategy for generating HBIM models starting from the integration of 3D thermal investigations and historical sources is proposed, concerning both the multi-temporal modification of the volumes of the building and the individual architectural elements. The case study analysed for the current research was the Palacio de Colomina in Valencia, Spain, a noble palace—now the headquarters of a university—that, during the last few centuries, has been subjected to considerable transformations in terms of rehabilitation works and modification of its volume.

This article aims to illustrate the importance of the contribution of 3D survey and modeling techniques in the framework of movable heritage and museum collection documentation and cataloguing. The work reported in this study focuses on digitalization and 3D modeling of some popular music instruments (belonging to the collection of Museo del Paesaggio Sonoro in Italy) in the framework of the SAMIC project (Sound Archives & Musical Instruments Collection). Different sensors and strategies have been applied during this research in order to obtain high-resolution digital replicas of the objects (a LiDAR system and a photogrammetric approach have been tested). The digital datasets modeled with the different techniques were compared in order to evaluate the quality of the models and their metric accuracy.