Archaeological sites characterized by the coexistence of extensive above-ground terrain and hypogeum structures present major challenges for accurate and comprehensive geospatial documentation. Conventional survey approaches-such as static terrestrial laser scanning (TLS), total-station measurements, and aerial photogrammetry-often suffer from operational constraints, particularly in the presence of narrow underground spaces, low or absent illumination, harsh environmental conditions, and restrictions on UAV deployment. Additional complexity arises when both surface and subterranean elements must be consistently georeferenced to a common global reference system, especially where establishing a traditional topographic-geodetic control network is impractical. Within the framework of the EIMAWA Egyptian-Italian Mission conducted by the University of Milano since 2018, the Geomatics group of the University of Bologna designed and implemented a multi-scale multi-technique 3D documentation workflow, with a prominent role assumed by Simultaneous Localization and Mapping (SLAM) mobile laser scanning. The approach was supported by GNSS measurements providing centimetric accuracy. SLAM was employed to document both the surface necropolis and multiple hypogeal tombs, enabling rapid acquisition of dense three-dimensional data in environments where traditional techniques are limited. All datasets were integrated within a unified reference system, resulting in a coherent, multi-layered spatial dataset representing both landscape and underground spaces. The results demonstrate that SLAM can produce dense point clouds that document at few-centimetric level accuracy and continuously both above- and below-ground contexts. Quantitative analyses of the co-registration and mutual alignment of multiple SLAM datasets confirm a high degree of internal consistency, further enhanced through post-processing refinement. Overall, the experience indicates that this solution represents a practical and reliable technique for complex archaeological surveying.

This study investigates the interplay between urban morphology, vegetation, and thermal environments by integrating mobile air temperature (AT) measurements with satellite-derived land surface temperature (LST). The case study is the city of Bologna (Italy). Correlation analysis revealed strong multicollinearity among morphological indicators, with building density and floor area ratio nearly collinear, while vegetation cover (PV) remained the most independent predictor. A composite urban density indicator (CUDI), derived through principal component analysis, was introduced to address redundancy among morphological metrics. Ordinary least squares regressions demonstrated significant associations, with PV exerting a pronounced cooling effect and CUDI amplifying both AT and LST. Model diagnostics confirmed statistical robustness, though residual spatial autocorrelation necessitated spatial regression approaches. Spatial lag models (SLMs) substantially improved explanatory power, highlighting spatial spillovers and neighborhood effects as central to understanding urban heat dynamics. Comparative analysis with spatial error models reinforced the dominance of SLM in capturing localized dependencies. Despite limitations in spatial coverage, temporal scope, and indicator transferability, findings emphasize the critical roles of vegetation and urban compactness in shaping thermal environments. This work underscores the necessity of integrating greening strategies with urban form management for effective heat mitigation and provides a methodological framework for analyzing urban heat islands through multi-source thermal and morphological data.

eXtended Reality (XR) technologies are increasingly employed for the visualization and communication of cultural heritage (CH), offering new opportunities for interpretation, documentation, and education. Within this context, the integration of XR with rigorous geomatic methodologies remains a relevant research topic, particularly for engineering-oriented applications. This paper presents a Mixed Reality (MR) application developed through a fully metric and integrated geomatic workflow in a university education context. The case study is the façade of the remaining structure of the San Salvatore ad Calchi medieval church, formerly identified as part of the Theodoric Palace, in Ravenna, Italy. Terrestrial Laser Scanning (TLS), close-range and unmanned aerial vehicle (UAV) photogrammetry, GNSS, and GIS data were systematically combined to produce an accurate three-dimensional documentation of the structure. A historical drawing from 1800’s – representing a previous constructive phase of the façade – was rectified using the obtained georeferenced orthophoto and transformed into a metrically consistent 3D model, enabling its in-situ superimposition onto the existing building within a MR environment. The application was developed in Unity and deployed on a Meta Quest 3 headset, permitting mutual visualization of real and virtual elements. Beyond the technical implementation, the study emphasizes the educational perspective. The complete Survey-to-XR workflow was integrated into a master’s-level Geomatics Engineering course focused on cultural heritage. Pre- and post-experience surveys indicate that XR enhances students’ understanding of integrated geomatic processes, spatial relationships, and data visualization and interpretation. The results support the use of MR as an effective educational tool when grounded in robust geomatic frameworks.

The integration of geomatic surveying and infrared thermography offers significant potential for non-destructive heritage diagnostics, yet thermographic data are often limited to qualitative visualization in 3D models. This study presents a replicable workflow for the numerical integration of thermal information through the creation of a thermal point cloud, where temperature is encoded as an attribute of each 3D point. The method combines close-range photogrammetry, terrestrial laser scanning, and thermography, and is tested on historic masonry in Bologna. Results demonstrate enhanced capabilities for spatially aware visualization and analysis of thermal data, supporting quantitative interpretation beyond conventional texture-based approaches.

Local climate zones (LCZs) provide a robust framework for understanding Urban Heat Island dynamics and for supporting climate-sensitive urban planning. Although widely adopted since their introduction in 2012, LCZ mapping remains constrained by urban morphology description and spectral separability among built-up classes. This study aims to strengthen the methodology to produce a high-resolution LCZ map by integrating multispectral (Sentinel-2, 10 m spatial resolution) and hyperspectral data (PRISMA, 30 m spatial resolution) with a suite of urban canopy parameters that describe the morphological and surface characteristics of the urban fabric, using a machine learning classification approach at finer spatial resolutions. The proposed approach is tested in the urban area of Bologna, Italy. The digitization of representative training and validation sites—which is one of the key challenges in accurate LCZ mapping, especially for spectrally heterogeneous classes—was conducted in a GIS environment by visual interpretation of high-resolution imagery with the aid of the Technical Map of the Municipality of Bologna. With the aim of strengthening the methodology, the present work tests different outlier-removal techniques on the training data and evaluates their impact on LCZ mapping performance. Finally, the Random Forest classifier was selected, and the workflow was implemented in a Python environment using the scikit-learn library. The results show that the classification achieved overall accuracy values of 0.79 using Sentinel-2 and 0.82 using PRISMA. Overall, the results show that urban morphology parameters are among the most important features. Training-sample refinement helped interpret the effect of sample heterogeneity, but LCZ classification performance was ultimately controlled by feature discriminative power, spatial resolution, and the intrinsic separability of each class.

Historical cartography constitutes a rich source of information on ancient landscapes. When managed in digital form and accurately georeferenced, it becomes an invaluable tool for investigating the history and evolution of urban contexts. Within an archaeological framework, the multidisciplinary integration of georeferenced historical cartography and archaeological data in a Historical Geo¬graphic Information System (HGIS) offers significant advantages. One of the key strengths of historical cartography lies in its capacity to contextualise, within past environments, the archaeological evidence brought to light through investigations, thereby supporting archaeologists in identifying and interpreting elements of the ancient city. The present study aims to exemplify this application of historical maps, focusing on two recently investigated archaeological sites in Ancona, Italy. This city is characterised by a rich archaeological heritage, much of which remains buried and unexplored, resulting in a high likelihood of encountering archaeological assets during public or private works. The proposed methodology, applicable to a wide range of cases, proves to be valuable not only for enhancing archaeological knowledge of urban areas but also for supporting urban planning, urban regeneration, and risk management. Historical Geographic Information Systems, enriched with archaeological data, constitute essential tools for guiding urban development and selecting optimal locations for new structures and infrastructure.

Digital Terrain Models (DTMs) are essential representations of Earth’s surface, widely used in topographic mapping, hydrological modeling, engineering, and hazard assessment. Advances in satellite imagery, LiDAR, and interpolation techniques have significantly improved DTM generation. This study evaluates the vertical accuracy of four global, freely available DEMs, SRTM 30, ALOS World 3D, Copernicus 30, as DSMs and FABDEM as a DTM, against high-resolution LiDAR-derived reference data across three diverse case study areas in Italy: urban, mountainous, and flat terrains. The assessment framework combined pixel-wise error statistics with zonal analysis using the 2023 ESRI Land Cover dataset. Results highlight that terrain morphology and land cover significantly affect DTM accuracy. Copernicus 30 and FABDEM outperformed the others overall, showing low mean elevation errors and consistent performance across landscape types. Copernicus 30 achieved high accuracy in urban areas (mean difference: 0.48 m), while FABDEM performed well in vegetated and mixed terrains (1.48 m and −0.87 m in Trentino-Alto Adige and Valle d’Aosta, respectively), benefiting from vegetation and building artifact removal. ALOS World 3D showed the poorest performance, with high errors in forested and urban areas, failing to meet its nominal vertical accuracy threshold (<5 m). SRTM 30, while less accurate than Copernicus 30 or FABDEM, remained within its expected accuracy (<16 m) and performed reliably in simpler terrains. Error distribution analysis confirmed these trends: Copernicus 30 showed tightly clustered, low-bias errors; FABDEM had broader but centered distributions; ALOS World 3D exhibited wide skewed errors. Zonal statistics further showed that dense vegetation and urban features caused the largest discrepancies, while bare ground yielded the highest accuracy. Overall, Copernicus 30 is recommended for urban applications, FABDEM for vegetated and mixed-use landscapes, and SRTM 30 for general terrain. ALOS World 3D is currently unsuitable for applications that require high vertical accuracy.

Urban and territorial development increasingly threatens the preservation of architectural heritage, often leading to degradation or loss. Many historic architectural works, once integral to community identity, now face the impacts of time and neglect. Addressing these challenges requires innovative solutions, and digital technologies offer significant potential for the conservation and enhancement of cultural heritage. This study employs reality-based 3D modeling techniques to generate accurate digital reconstructions of the Church of Santa Croce in Ravenna (Italy), used here as a case study to demonstrate a workflow for phasing analysis and interactive visualization of architectural transformations. The objective is not to produce a philological reconstruction, but to propose a methodological approach for digitally documenting and visualizing, with geometric rigor, the different constructive phases of historical buildings that have undergone structural changes. Using a spatial–temporal navigation approach, the research explores the various historical phases of the site within an interactive 3D virtual environment. The resulting platform facilitates both scholarly investigation and public engagement by providing immersive visualization and enhanced understanding of the monument’s transformation over time. Through this case study, the project underscores the critical role of contemporary digital tools in cultural heritage conservation and explores novel methods for communicating and disseminating historical knowledge.

Archaeological research fundamentally relies on detecting features to uncover hidden historical information. Airborne (aerial) LiDAR technology has significantly advanced this field by providing high-resolution 3D terrain maps that enable the identification of ancient structures and landscapes with improved accuracy and efficiency. This technical note comprehensively reviews 45 recent studies to critically examine the integration of Machine Learning (ML) and Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNNs), with airborne LiDAR derivatives for automated archaeological feature detection. The review highlights the transformative potential of these approaches, revealing their capability to automate feature detection and classification, thus enhancing efficiency and accuracy in archaeological research. CNN-based methods, employed in 32 of the reviewed studies, consistently demonstrate high accuracy across diverse archaeological features. For example, ancient city walls were delineated with 94.12% precision using U-Net, Maya settlements with 95% accuracy using VGG-19, and with an IoU of around 80% using YOLOv8, and shipwrecks with a 92% F1-score using YOLOv3 aided by transfer learning. Furthermore, traditional ML techniques like random forest proved effective in tasks such as identifying burial mounds with 96% accuracy and ancient canals. Despite these significant advancements, the application of ML/DL in archaeology faces critical challenges, including the scarcity of large, labeled archaeological datasets, the prevalence of false positives due to morphological similarities with natural or modern features, and the lack of standardized evaluation metrics across studies. This note underscores the transformative potential of LiDAR and ML/DL integration and emphasizes the crucial need for continued interdisciplinary collaboration to address these limitations and advance the preservation of cultural heritage.

This study explores the potential of integrating multi-source remote sensing data—including Sentinel-1 synthetic aperture radar (SAR) imagery, Sentinel-2 optical imagery, and Landsat 8 thermal data—for crop classification in Emilia-Romagna (Northern Italy). Using satellite imagery and agricultural surveys, we constructed a temporal dataset covering 2020 with 27 biweekly time steps. After filtering out underrepresented crop types with insufficient samples for machine learning training, nine crop types remained. We implemented four deep learning models using TensorFlow: Dense Neural Network (DNN), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Transformer. Our results indicate that removing underrepresented crops significantly improves classification performance, leading to an overall accuracy of approximately 91%. Incorporating Landsat 8 thermal data further enhanced accuracy, with the Transformer model achieving a peak accuracy of 92.08%. A crop-specific analysis revealed that temperature observations notably improved classification for crops with distinct thermal signatures (e.g., sugar beets, corn), whereas limited improvement was observed for spectrally similar cereals (e.g., wheat, barley). Overall, the Transformer model demonstrated exceptional ability in capturing spatial-temporal dependencies in multivariate time-series data. These findings underscore the advantages of integrating multi-source satellite data including thermal infrared and leveraging attention-based neural networks for large-scale agricultural monitoring and resource management.

This study investigates vertical soil movement, a subsidence phenomenon affecting infrastructure and communities in the Emilia-Romagna region (Italy). Building upon previous research—initially based on leveling and GNSS observations and later expanded with interferometric synthetic aperture radar (InSAR)—this study focuses on recent data from 2016 to 2021. A key innovation is the use of dual-geometry ascending and descending acquisitions to derive the vertical and the east–west movement components, a technique not previously applied at a regional scale in this area. The integration of advanced geodetic techniques involved processing 1208 Sentinel-1 satellite images with the SqueeSAR® algorithm and analyzing data from 28 GNSS permanent stations using the precise point positioning (PPP) methodology. By calibrating the InSAR data with GNSS measurements, we generated a comprehensive subsidence map for the study period, identifying trends and anomalies. The analysis produced 13.5 million measurement points, calibrated and validated using multiple GNSS stations. The final dataset, processed through geostatistical methods, provided a high-resolution (100-m) regional subsidence map covering nearly 11,000 square kilometers. Finally, the vertical soil movement map for 2016–2021 was developed, featuring isokinetic curves with an interval of 2.5 mm/year. The results underscore the value of integrating these geodetic techniques for effective environmental monitoring in subsidence-prone areas. Furthermore, comparisons with previous subsidence maps reveal the evolution of soil movement in Emilia-Romagna, reinforcing the importance of these maps as essential tools for precise subsidence monitoring.

High-resolution Digital Terrain Models (DTMs) are essential for precise terrain analysis, yet their production remains constrained by the high cost and limited coverage of LiDAR surveys. This study introduces a deep learning framework based on a modified Residual Channel Attention Network (RCAN) to super-resolve 10 m DTMs to 1 m resolution. The model was trained and validated on a 568 km2 LiDAR-derived dataset using custom elevation-aware loss functions that integrate elevation accuracy (L1), slope gradients, and multi-scale structural components to preserve terrain realism and vertical precision. Performance was evaluated across 257 independent test tiles representing flat, hilly, and mountainous terrains. A balanced loss configuration (α = 0.5, γ = 0.5) achieved the best results, yielding Mean Absolute Error (MAE) as low as 0.83 m and Root Mean Square Error (RMSE) of 1.14–1.15 m, with near-zero bias (−0.04 m). Errors increased moderately in mountainous areas (MAE = 1.29–1.41 m, RMSE = 1.84 m), confirming the greater difficulty of rugged terrain. Overall, the approach demonstrates strong potential for operational applications in geomorphology, hydrology, and landscape monitoring, offering an effective solution for high-resolution DTM generation where LiDAR data are unavailable.

Cultural heritage encompasses both tangible and intangible aspects for each of us, and efforts must be made to safeguard this legacy for future generations. Unfortunately, in addition to natural and environmental degradation, human activities pose a significant threat to the integrity of historical sites. Monuments and architecture have frequently been intentionally destroyed in conflict zones all over the world. Three-dimensional (3D) and virtual technologies can serve as tools to digitally preserve these sites and raise awareness about the importance of historical properties to the general public, particularly when physical sites are at risk or no longer exist. This challenging field of lost heritage is the framework of this project, in which a procedure of geomatics-based techniques such as spherical photogrammetry, 3D modelling and virtual reality (VR) technologies was developed to reconstruct lost historical architecture. The Roman Theatre in Palmyra, Syria, partially destroyed during Syria's war in 2017, serves as a case study. The methodology report starts with the description of the metrological foundation of the 3D model construction, i.e. spherical photogrammetry as developed by Prof. Fangi (Marche Polytechnic University). Then, the geometry optimisation phase carried out to accomplish the VR limitations in terms of polygon count is presented. Ultimately, the procedure for the virtual environment construction is explained, as well as the development of a metaverse scenario to be visited and shared on an online-based platform. This collective virtual experience aims to revive the destroyed architecture and communicate its significance to the public through a collective and interactive virtual exploration. This study also includes experiments to assess user response, providing insights into methodology effectiveness in conveying Palmyra's Theatre historical relevance and shedding light on the users' perceptions of virtual tools usage for lost heritage dissemination. The evaluation questionnaire's results will guide the project's future developments.

In recent years, increasing attention has been directed toward the application of digitization through geomatic-based technologies for museum assets. These powerful tools have proven valuable in assisting museums in the dissemination of cultural heritage. Additionally, museums around the world are implementing strategies to improve the accessibility of their assets by involving the use of 3D digital reconstruction. The 3D high-precision survey is employed in several fields to scan objects with a geometrical accuracy up to the micrometer level. These technologies come into play when dealing with detailed surfaces and complex geometry, as often occurs with cultural heritage assets. This paper presents a set of experiences in high-precision 3D scanning and post-processing operations in the framework of a project at the Territory Museum of Riccione (Italy). The 3D data acquisition methodology conducted and digital operations are reported on for some of the scanned artifacts.

High-resolution Digital Terrain Models (DTMs) are essential for precise terrain analy-sis, yet their production remains constrained by the high cost and limited coverage of LiDAR surveys. This study introduces a deep learning framework based on a modified Residual Channel Attention Network (RCAN) to super-resolve 10 m DTMs to 1 m res-olution. The model was trained and validated on a 568 km² LiDAR-derived dataset us-ing custom elevation-aware loss functions that integrate elevation accuracy (L1), slope gradients, and multi-scale structural components to preserve terrain realism and ver-tical precision. Performance was evaluated across 257 independent test tiles repre-senting flat, hilly, and mountainous terrains. A balanced loss configuration (α = 0.5, γ = 0.5) achieved the best results, yielding Mean Absolute Error (MAE) as low as 0.83 m and Root Mean Square Error (RMSE) of 1.14–1.15 m, with near-zero bias (–0.04 m). Er-rors increased moderately in mountainous areas (MAE = 1.29–1.41 m, RMSE = 1.84 m), confirming the greater difficulty of rugged terrain. Overall, the approach demonstrates strong potential for operational applications in geomorphology, hydrology, and land-scape monitoring, offering an effective solution for high-resolution DTM generation where LiDAR data are unavailable.

Climate change effects have become increasingly visible recently through extreme weather events, such as heat waves. These are strictly related to a two-way relationship with urbanization. Indeed, urban expansion due to population migration from rural to urban areas impacts energy consumption, soil sealing with vegetation loss and gas emissions. Moreover, due to their characteristics, cities experience the typical urban heat island microclimate and are more vulnerable to heatwaves. In this context, having insight into the land surface temperature and accurate knowledge of city characteristics is essential to wise decision-making to ensure a more sustainable livelihood. The present paper provides an overview of two different approaches useful for thermal mapping at the city scale, implementing GIS-based analysis integrating local surveys with geospatial data. In particular, the city of Bologna (Italy) is studied. In the first study, temperature measurements along a transect was taken on March 19, 2021, with a mobile system. Then they were corrected considering data from some weather stations interpolated with Kriging, which shows the highest correlation coefficient of 0.99. The corrected temperature correlated at 0.69 with remote sensing NDVI data. The second study analyzed the significant impact of urban morphology, particularly building density, on temperature variations; it emphasizes the need for strategic urban planning to mitigate the Urban Heat Island effect.

The World Health Organization (WHO) recommends the introduction of a water safety plan (WSP) approach on drinking water, in all types of settings. This study represents the first WSP developed on the Neptune Fountain, in Bologna (Italy), based on an interdisciplinary approach, integrating hydraulic and microbiological features, in a Building Information Modeling (BIM). The aim was to develop a dynamic and digital platform to update and share the maintenance program, promoting collaboration among microbiologists, engineers, and municipal staff. Water samples were collected along fountain water distribution systems (WDS) from 2016 to 2021 to monitor water quality through the heterotrophic bacteria at 22 °C and 37 °C, as well as to conduct an Enterococci, Coliform bacteria, Escherichia coli, Pseudomonas aeruginosa, Clostridium perfringens, and Staphylococcus aureus assessment. Simultaneously, hydraulic measures were performed, and advanced geomatics techniques were used to detect the WDS structural components, with a focus on the water treatment system (WTS). The WTS consisted of 10 modules corresponding to specific treatments: descaling, carbon–sand filtration, reverse osmosis, and ultraviolet disinfection. Fecal indicators, heterotrophic bacteria, and P. aeruginosa exceeded the reference limits in most of the modules. Several disinfections and washing treatments, other than changing the maintenance procedure scheduling, were performed, improving the WTS and controlling the contamination. The developed microbiological results, hydraulic measurements, and maintenance procedures were integrated in the BIM model to optimize the data storage, updating procedures and the real-time data sharing. This approach improved the fountain management, operation, and material conservation, ultimately preserving the health of daily visitors.

In this paper, a convenient workflow to obtain a simplified lightweight IFC description of a dense point cloud describing a historic building is proposed. The proposed procedure relies on slicing of the point cloud that aims at reconstructing the geometry of the solid. The resulting slices can then be easily employed in the generation of finite element (FE) and architectural building information modeling (BIM) models. In particular, the FE model generation procedure guarantees the obtainment of a conforming solid FE mesh ready to be used for structural purposes. Contextually, the architectural BIM model generation is achieved by slices extrusion to obtain the overall volume of the building. Then, the assembly of independent slice-based meshes is subjected to re-topology to obtain the overall watertight bounding surface of the building. A real historic structure is here used to show the effectiveness of the FE and BIM model generation procedures. Finally, the BIM model generation of an actual historic structure is carried out through the proposed workflow.

The evolution of digitisation technologies and the growing transition to the digital age have reshaped the cultural heritage landscape, expanding possibilities beyond digital documentation or additionally immersive interactive experiences. This transition has also fostered innovative approaches for the (re)use of cultural heritage digital data. This paper proposes an integrated approach of digital and virtual technologies, by effectively integrating and (re)using different available digital data with the final aim of reconstructing a selected portion of the Tiwanaku UNESCO complex in a Virtual Environment, with work still in progress. The research methodology involved gathering 2D and 3D digital products for the 3D modeling of two areas within Tiwanaku, namely the Kalasasaya and the Semi-Subterranean temples, and the 3D reconstruction of their monuments. Finally, all the reconstructed elements were integrated into a Virtual Environment where the virtual experience goes beyond mere site navigation. It provides an immersive and interactive heritage learning experience through object interaction, historical context storytelling, and also offers a unique opportunity to imagine how the ancient city of Tiwanaku looked like by introducing some 3D-reconstructed monuments to the modeled site in their original configuration and position.