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.

Urban greenery is essential for environmental quality, visual comfort, and residents’ well-being, and it becomes especially critical in high-density residential compounds where outdoor space is limited. This study proposes a pedestrian-scale visibility framework that integrates solid 3D models (DEM, extruded buildings, water) with voxelized LiDAR point clouds to reconstruct fine-resolution outdoor scenes and to quantify visual perception indicators, including green view factor (GVF), sky view factor (SVF), and average green distance (AGD). A residential community in Nanjing is used as the case study. Line-of-sight sampling was performed on 223 viewpoints distributed across three empirically identified activity zones, and a resident questionnaire was conducted in parallel (279 valid responses). The results show that the visually open zone, characterized by relatively high SVF, moderate GVF, and larger vegetation setback (higher AGD), is also the zone most preferred by residents, whereas the zone with the highest GVF but strong enclosure is least preferred. This consistency between modeled indicators and survey responses confirms that excessive, close-range planting may reduce usability, while a balanced combination of greenery and openness better supports everyday outdoor activities. The proposed Point-Cloud-Based approach, therefore, provides a data-driven basis for planning, evaluating, and managing outdoor environments in dense urban residential areas, and ultimately reaching the purpose of more livable urban communities in the era of intelligent and sustainable cities.

Unmanned aerial vehicles (UAVs) are widely used for documentation, surveying, and 3D modeling in the built environment, yet their outputs often remain difficult to reuse for immersive comparison of alternative construction scenarios. This study presents a low-cost UAV-to-3DGS-to-VR workflow for constructing scenario-comparable immersive environments for built-environment review. The workflow combines multi-angle UAV imagery, point-cloud-based geometric anchoring, 3D Gaussian Splatting (3DGS), and Unity-based virtual reality (VR) to transform drone-captured reality into a reusable scene for controlled scenario comparison. The workflow is demonstrated in Middenbeemster, the central town of the Beemster polder World Heritage property. One present-condition scene (M0) and three alternative construction scenarios (M1 to M3) were created within a shared spatial reference. Reconstruction quality was assessed using PSNR and SSIM, and the VR scenes were further evaluated through eye-tracking, head-motion recording, and subjective ranking. The results indicate that the workflow can generate visually reliable and directly comparable immersive scenes from UAV data in this case study. Behavioral and subjective findings showed a consistent pattern, with M1 appearing more compatible than M2 and M3 in this pilot evaluation. The study contributes a pilot UAV-based workflow that links reality capture, immersive scenario comparison, and supplementary behavioral evidence within one process.