Historic buildings hold significant cultural value and their repair and protection require diverse approaches. With the advent of 3D digitalization, drones have gained significance in heritage studies. This research focuses on applying digital methods for restoring architectural heritage. It utilizes non-contact measurement technology, specifically unmanned aerial vehicles (UAVs), for data collection, creating 3D point cloud models using heritage building information modeling (HBIM), and employing virtual reality (VR) for architectural heritage restoration. Employing the “close + surround” oblique photography technique combined with image matching, computer vision, and other technologies, a detailed and comprehensive 3D model of the real scene can be constructed. It provides crucial data support for subsequent protection research and transformation efforts. Using the case of the Santo Stefano Church in Volterra, Italy, an idealized reconstructed 3D model database was established after data collection to preserve essential resources such as the original spatial data and relationships of architectural sites. Through the analysis of relevant historical data and the implementation of VR, the idealized and original appearance of the case was authentically restored. As a result, in the virtual simulation space, the building’s style was realistically displayed with an immersive experience. This approach not only safeguards cultural heritage but also enhances the city’s image and promotes tourism resources, catering to the diverse needs of tourists.

The construction industry faces persistent challenges, including labor shortages and safety hazards, while traditional construction methods are increasingly strained by the complexity and sustainability demands of modern projects. The integration of robotics shows significant potential for mitigating labor shortages and enhancing safety on construction sites. The current adoption of robotics technologies is driven by both the maturity of robotics technology and the potential for cost reduction compared with manual labor. This review synthesizes recent advancements and trends in construction robotics through a bibliometric analysis of 212 publications indexed in Web of Science from 2002 to 2024. Key findings indicate a 320% increase in research output from 2015 to 2022, with dominant clusters focusing on autonomous navigation, human–robot collaboration, and sustainability-driven automation. Geographically, China and the United States lead in number of publications, with 67 and 65 articles, respectively; however, cross-border collaborations remain sparse, constituting fewer than 5% of co-authored papers. Keyword co-occurrence analysis reveals evolving priorities, including artificial intelligence (AI)-driven adaptive control, modular prefabrication, and the ethical implications of automation. Despite technological advancements, critical gaps remain in terms of interoperability, workforce retraining, and regulatory frameworks. This study emphasizes the need for interdisciplinary integration, standardized protocols, and policy alignment to bridge the divide between academic innovation and industry adoption, ultimately facilitating the global transition toward Construction 4.0.

Urban heritage documentation often separates 3D data acquisition from immersive interaction, limiting both accuracy and user impact. This study develops and validates an end-to-end workflow that integrates UAV photogrammetry with terrestrial LiDAR and deploys the fused model in a VR environment. Applied to Piazza Vittorio Emanuele II in Rovigo, Italy, the approach achieves centimetre-level registration, completes roofs and upper façades that ground scanning alone cannot capture, and produces stable, high-fidelity assets suitable for real-time interaction. Effectiveness is assessed through a three-layer evaluation framework encompassing vision, behavior, and cognition. Eye-tracking heatmaps and scanpaths show that attention shifts from dispersed viewing to concentrated focus on landmarks and panels. Locomotion traces reveal a transition from diffuse roaming to edge-anchored strategies, with stronger reliance on low-visibility zones for spatial judgment. Post-VR interviews confirm improved spatial comprehension, stronger recognition of cultural values, and enhanced conservation intentions. The results demonstrate that UAV-enabled completeness directly influences how users perceive, navigate, and interpret heritage spaces in VR. The workflow is cost-effective, replicable, and transferable, offering a practical model for under-resourced heritage sites. More broadly, it provides a methodological template for linking drone-based data acquisition to measurable cognitive and cultural outcomes in immersive heritage applications.

The visual aesthetics of villages are remarkably affected by the exterior quality of traditional residences, influencing the impression and assessment of local culture. A proper scientific assessment of exterior quality can protect traditional cultures and improve the development of villages. This research was conducted in a village consisting of 115 residences (Mengjinglai village, which is on the border between China and Myanmar). The backpropagation (BP) neural network model with genetic algorithm (GA) was applied to evaluate the quality of the dwellings. All the evaluation values of the dwellings were defined by scores. Meanwhile, the score of each residence was affected by three main factors: architectural spatial elements, architectural construction elements, and historical and cultural elements. The results show that the village’s dwellings are well preserved and clearly express the traditional Dai style. Moreover, the GA–BP approach is more suitable than the traditional BP method for the assessment of the exterior quality. The quantitative machine learning model would be useful for other aspects of the assessment of similar villages in the future.

This study presents an innovative virtual reality (VR) framework for the digital preservation and interactive exploration of Rovigo’s architectural heritage in Italy, focusing on its medieval city walls and their transformation over centuries. By integrating multi-source data fusion (aerial photogrammetry, laser scanning, and close-range photogrammetry), the framework achieves high-precision 3D reconstruction, capturing intricate details at millimeter-level accuracy. The immersive VR experience employs embodied interaction technologies—including gesture recognition (Leap Motion), full-body tracking (HTC Vive), and voice commands—to enable users to engage with historical narratives through hands-on activities such as simulating medieval construction techniques, participating in 19th-century demolition debates, and uncovering hidden traces in residential buildings.The framework combines linear and non-linear storytelling to balance educational rigor with user autonomy, fostering deeper connections to cultural heritage. Key contributions include: (1) a scalable methodology for heritage digitization, demonstrated through Rovigo’s case; and (2) the novel integration of embodied interaction with dynamic narrative design, enhancing engagement and critical reflection on preservation challenges. Preliminary results highlight the potential of VR to bridge the gap between historical documentation and public participation.Limitations, such as data occlusion complexities and scalability for larger sites, are discussed, alongside future directions involving mixed reality (MR), augmented reality (AR), and AI-generated content (AIGC) for personalized storytelling. This research advances digital heritage practices by transforming static monuments into interactive, emotionally resonant experiences while setting a precedent for global applications.