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.

Incremental urban and community expansion in rural heritage landscapes often produces cumulative visual impacts, yet planning rarely specifies a clear endpoint for acceptable change. This paper proposes an integrated Visual Impact Assessment (VIA) framework, aligned with SDG 11, to determine “when to stop” using stage-comparable evidence across past, present, and future conditions. The framework is organized in three modules. First, a point cloud-enhanced GIS module quantifies visibility and spatial change across development stages. Second, an enhanced Key Observation Point (KOP) module derives matched eye-level evidence from multi-temporal street-level panoramas and scenario visualizations, for example using Street View Imagery (SVI) time series and 3D Gaussian Splatting (3DGS) rendering. Third, a decision layer integrates structured public acceptability from a questionnaire covering different respondent groups with in-depth expert interviews and synthesis, with virtual reality (VR) eye- and head-tracking used as supportive behavioral evidence. Applied to the Middenbeemster expansion in the Beemster Polder, the Netherlands, the framework yields a case-calibrated reference package for decision support: KOP-based construction intensity serves as the primary reference line for review, perception indicators serve as supporting guardrails, spatial character metrics act as case-specific reference checks to protect the polder framework, and visibility diagnostics remain a necessary screening layer. More broadly, the framework provides a transparent and replicable procedure that can be transferred and locally recalibrated for heritage-sensitive rural-urban fringes where change is incremental and cumulative, supporting a stage-comparable VIA approach.

Street view imagery (SVI) is widely used in urban visual analysis and often treated as equivalent to eye-level perception. Yet its limitations and contextual applicability remain underexplored. This paper conducts a diagnostic viewpoint-level comparison of an image-based SVI pipeline and a 3D model-based field-of-view (FOV) method to clarify their respective weaknesses, strengths, and how they can be combined in practice (rather than treated as interchangeable or numerically fused). Using the West Lake ring road in Hangzhou as a case study, we analyze 2140 panoramas at 1075 viewpoints. The comparison shows systematic differences: SVI produces higher green shares (+0.16 on average), while FOV yields higher paved ground (+0.13) and building shares (+0.08). Sky differs little overall, water remains minor, and cross-method consistency varies by segment; SVI displays greater local variability linked to canopy occlusion and near-field heterogeneity. A small perception survey validates these findings. Terrain relief and building height were recognized more consistently in FOV, while vegetation and water abundance aligned more closely with SVI. Participants also judged overall ambience more easily from FOV’s structural stability, even though SVI conveyed greater visual realism. These results reveal clear complementarities: FOV provides structure-aware metrics, SVI emphasizes appearance cues, and neither alone captures lived perception. On this basis, we propose a combination-oriented three-layer workflow, with perception as a required validation layer to support reliable applications in skyline and openness control, interface and character management, greenery maintenance, and equity assessment.

Achieving sustainability in prefabricated construction necessitates a balance between operational efficiency and stringent environmental constraints. However, cascading rework chains triggered by assembly defects frequently disrupt this equilibrium. Existing literature predominantly addresses this dynamic through reactive rescheduling, thereby largely overlooking the potential of proactive topological interception. To bridge this gap, this study proposes a proactive bi-level scheduling framework that mathematically integrates strategic quality inspection planning with operational low-carbon project execution. Specifically, a Generalized Total Cost (GTC) model is formulated to internalize multi-objective trade-offs—including time, cost, and carbon emissions—into a unified financial metric through market-based shadow prices. This framework is operationalized through a novel bi-level Hybrid Evolutionary Algorithm (H-TS-CDBO). By combining the global exploration capabilities of Chaotic Dung Beetle Optimization with the local refinement mechanisms of Tabu Search, the proposed solver is specifically engineered to navigate the topological ruggedness induced by proactive inspection interventions. Empirical benchmarking validates the computational robustness of the solver, while an illustrative case study substantiates a critical managerial paradigm shift from “passive remediation” to “active prevention”: compared to traditional methods, a marginal preventive investment of 5.4% functions as an effective containment mechanism, yielding a 40.8% net reduction in the GTC. Furthermore, a sensitivity analysis regarding varying static carbon tax rates simulates algorithmic adaptation under diverse regulatory intensity thresholds, delineating an actionable pathway for project managers to achieve lean, low-carbon synergy amidst evolving regulatory pressures.

Historic urban areas (HUAs) are visually and culturally sensitive environments where blue-green infrastructure (BGI) plays an increasingly important role in shaping spatial identity and environmental quality. While BGI's ecological functions are well documented, its influence on human visual perception, particularly within HUAs, remains largely unexplored. Addressing this gap, this paper proposes an integrative framework to assess how BGI affects visual experiences in heritage contexts, bridging methodological, perceptual, and user-group dimensions. By combining UAV-based photogrammetry with a three-layered perception model, the research integrates spatial analysis and empirical methods across seeing (eye-tracking), feeling (questionnaire), and understanding (interviews) layers. Street-level BGI exposure was spatially quantified and used to inform perception experiments involving both expert and general public groups. This multi-methodological, multi-layered, cross-group approach extends existing research by providing a comprehensive examination of BGI's visual impact at different cognitive levels, particularly within historic settings. Findings reveal that BGI enhances perceptual diversity, visual preference evaluation, and cognitive engagement across both groups. Although it may slightly divert attention from dominant heritage features, BGI fosters broader visual exploration and higher environmental ratings. Experts interpret BGI through more systemic and functional perspectives, while the public emphasizes emotional, aesthetic, and recreational values. Overall, this study presents a replicable framework integrating digital spatial modeling with layered perception analysis, offering new insights for evaluating and enhancing visual environments in HUAs. It supports more inclusive visual assessments and provides a basis for informed planning and selective design interventions in heritage contexts.

Urban heritage landscapes, with their layered cultural and aesthetic values, require precise visual analysis to support conservation and planning. However, existing visual analysis methods are often fragmented and fail to fully capture their complex visual-spatial characteristics. To address this gap, this paper proposes a combined visual analysis framework that integrates four GIS-based visual analysis methods—cumulative viewshed (CV), visual magnitude (VM), field of view (FOV) analysis, and street-view image (SVI) segmentation. These methods were applied to the UNESCO World Heritage Site of West Lake in Hangzhou, China, to explore lake–city–landscape relationships, classify lakeside landscape types, and interpret the spatial composition of iconic viewpoints. Findings indicate that: (a) four zones with both high CV and VM values coincide with key architectural and scenic landmarks, suggesting intentional spatial design strategies, while half of the “Ten Scenic Places” are influenced by symbolic or experiential factors beyond visibility; (b) 37 landscape types were identified along lakeside roads, revealing areas where vegetation obscures potential lake views and where design trade-offs are evident; (c) only two of ten potential city-to-lake visual corridors remain unobstructed, pointing to unmanaged vegetation as a critical barrier; and (d) these insights inform targeted visual management strategies, including vegetation control, viewpoint activation, and circulation optimization. This study highlights the limitations of single-method approaches, such as SVI's insensitivity to topographic variation, and suggests that a multi-perspective integration of VAMs can yield deeper spatial insights and more actionable guidance for managing urban heritage landscapes.

This systematic literature review critically examines the application of digital technologies in architectural heritage risk management from 2014 to 2024, focusing exclusively on English-language publications. As the significance of architectural heritage continues to be recognized globally, there is an increasing shift towards integrating digital solutions to ensure its preservation and management. This paper explores the evolution and application of digital technologies such as Building Information Modeling (BIM), Geographic Information Systems (GIS), and advanced imaging techniques within the field. It highlights how these technologies have facilitated the non-destructive evaluation of heritage sites and enhanced accessibility and interaction through virtual and augmented reality applications. By synthesizing data from various case studies and scholarly articles, the review identifies current trends and the expanding scope of digital interventions in heritage conservation. It discusses the interplay between traditional conservation approaches and modern technological solutions, providing insights into their complementary roles. The analysis also addresses the challenges and limitations encountered in the digital preservation of architectural heritage, such as data integration, the compatibility of different technologies, and the need for more comprehensive frameworks to guide the implementation of digital tools in heritage conservation practices. Ultimately, this review underscores the transformative impact of digital technology in managing architectural heritage risks, suggesting directions for future research and the potential for innovative applications in the field.

This paper investigates the role of 3D Gaussian Splatting (3DGS) within point cloud–dominated workflows for modern architectural heritage digitization. While 3DGS enables real-time, photorealistic visualization, its integration into LiDAR-based documentation pipelines remains underexplored. Using Bouwpub, a modern heritage building in the Netherlands, as a case study, the paper compares 3DGS and LiDAR across data acquisition and preservation, visualization, semantic segmentation, and dissemination. Results show that 3DGS offers superior visual expressiveness and user responsiveness, whereas LiDAR provides greater structural accuracy and segmentation reliability. Based on these findings, two integration strategies are proposed: a Blender-based multi-angle rendering workflow and a Level of Detail 3DGS (LOD3DGS) pipeline. Moving from isolated assessment to applied integration, the study positions 3DGS as a complementary visualization and dissemination module rather than a replacement. This hybrid approach supports immersive, scalable, and semantically enriched digital heritage systems, offering new directions for enhancing both expert documentation and public engagement.

Highlights: What are the main findings? An end-to-end workflow integrates UAV photogrammetry, LiDAR, and VR for heritage. Three-layer evaluation shows focused attention, edge-anchored movement, and clearer cultural understanding. What is the implication of the main finding? UAV-enabled completeness improves both geometric fidelity and user experience in VR. The workflow is affordable and transferable, supporting under-resourced heritage sites. 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.

3D Gaussian Splatting (3DGS) is an advanced 3D representation method that enhances point clouds by incorporating spectral image content, enabling high-fidelity heritage documentation. A 3DGS visualization presents these enriched Gaussians, ensuring high geometric accuracy, detailed texture representation, and efficient spatial reconstruction, thereby enhancing both precision and efficiency in digital heritage preservation. This paper applies 3DGS to modern architectural heritage, combining UAV-based data acquisition with advanced rendering techniques. Using the Delft University of Technology Aula and Library buildings as case study, the research establishes a workflow for efficient heritage documentation and visualization.