Search Results (279)

Augmented reality (AR) offers a novel way to enrich museum visits by deepening engagement and enhancing learning. This study presents the development of a mobile application for the Abylkhan Kasteyev State Museum of Arts (Almaty, Kazakhstan), designed to recognize and visualize exhibits through AR. Using computer vision and machine learning, the application identifies artifacts via a smartphone camera and overlays interactive 3D models in an augmented environment. The system architecture integrates Flutter plugins for AR rendering, YOLOv8 for exhibit recognition, and a cloud database for dynamic content updates. This combination enables an immersive educational experience, allowing visitors to interact with digital reconstructions and multimedia resources linked to the exhibits. Pilot testing in the museum demonstrated recognition accuracy above 97% and received positive feedback on usability and engagement. These results highlight the potential of AR-based mobile applications to increase accessibility to cultural heritage and enhance visitor interaction. Future work will focus on enlarging the exhibit database, refining performance, and incorporating additional interactive features such as multi-user collaboration, remote access, and gamified experiences. Full article

Studying Geography and Geology in formal education involves learning about landforms, landscapes, and the importance of preserving Natural Heritage. Geomorphosites are valuable resources with significant educational potential, as they can help students understand Earth’s history, appreciate Natural Heritage, and recognize their cultural and historical connections to the territory. By analyzing the curricular contents of the educational systems in Spain and Italy, this research proposes a novel method, focused on formal education, to assess the didactic potential of geomorphosites. The assessment is based on (i) how representative geomorphosites are of physical processes, (ii) their potential to support interdisciplinary learning, (iii) the availability of didactic materials, and (iv) the possibility of applying field techniques. The results revealed varying levels of didactic suitability among the analyzed geomorphosites, with Ulaca Hill (Spain) and Nirano mud volcanoes (Italy) having the highest didactic potential, also allowing evaluation of their relevance across different educational stages. The application of the methodology in both the Spanish and Italian contexts, with minor adjustments, showed that this approach is readily adaptable to other educational systems. Overall, this study provides a transferable framework for integrating geomorphological heritage into formal teaching, promoting place-based learning and fostering awareness and conservation of Natural Heritage. Full article

Eight Views is a time-honored East Asian cultural-landscape paradigm in which eight emblematic natural—cultural scenes fuse regional character, historical memory, and aesthetic ideals into a coherent narrative. It encodes the collective memory and identity of a city (or garden/region), a premodern ‘mental map’ or proto- ‘city brand’. In China, the historic Urban Eight Views are rooted in local environments and traditions and constitute significant, high-value landscape heritage today. Yet rapid urbanization has inflicted severe physical damage on these ensembles. Coupled with insufficient holistic and systemic understanding among managers and the public, this has led, during development and conservation alike, to spatial insularization, fragmentation, and even disappearance, alongside widening divergences in cultural cognition and biases in value judgment. Taking Longmen Haoyue in Chongqing, one of the historic Urban Eight Views, as a case that manifests these issues, this study develops a holistic–relational approach for the urban, historical Eight Views and explores landscape-based pathways to protect the spatial structure and cultural connotations of the heritage that has been severely damaged and is in a state of disappearance or semi-disappearance amid modernization. Methodologically, we employ decomposition analysis to extract the historical information elements of Longmen Haoyue and its internal relational structure and corroborate its persistence through field surveys. We then apply the FAHP method to grade the conservation value and importance of elements within the Eight Views, quantitatively clarifying protection hierarchies and priorities. In parallel, a multidimensional corpus is constructed to analyze online dissemination and public perception, revealing multiple challenges in the evolution and reconstruction of Longmen Haoyue, including symbolic misreading and cultural decontextualization. In response, we propose an integrated strategy comprising graded element protection and intervention, reconstruction of relational structures, and the building of a coherent cultural-semantic and symbol system. This study provides a systematic theoretical basis and methodological support for the conservation of the urban historic Eight Views cultural landscapes, the place-making of distinctive spatial character, and the enhancement of cultural meanings. It develops an integrated research framework, element extraction, value assessment, perception analysis, and strategic response that is applicable not only to the Eight Views heritage in China but is also transferable to World Heritage properties with similar attributes worldwide, especially composite cultural landscapes composed of multiple natural and cultural elements, sustained by narrative traditions of place identity, and facing risks of symbolic weakening, decontextualization, or public misperception. Full article

Jiangnan water towns, as distinctive cultural landscapes in China, are confronting the dual challenge of surging tourist flows and imbalances in spatial distribution. Research on pedestrian dynamics has so far offered narrow coverage of influencing factors and limited insight into underlying mechanisms, falling short of a systemic perspective and an interpretable theoretical framework. This study uses Nanxun Ancient Town as a case study to address this gap. Pedestrian trajectories were captured using temporarily installed closed-circuit television (CCTV) cameras within the scenic area and extracted using the YOLOv8 object detection algorithm. These data were then integrated with quantified environmental indicators and analyzed through Random Forest regression with SHapley Additive exPlanations (SHAP) interpretation, enabling quantitative and interpretable exploration of pedestrian dynamics. The results indicate nonlinear and context-dependent effects of environmental factors on pedestrian dynamics and that tourist flows are jointly shaped by multi-level, multi-type factors and their interrelations, producing complex and adaptive impact pathways. First, within this enclosed scenic area, spatial morphology—such as lane width, ground height, and walking distance to entrances—imposes fundamental constraints on global crowd distributions and movement patterns, whereas spatial accessibility does not display its usual salience in this context. Second, perceptual and functional attributes—including visual attractiveness, shading, and commercial points of interest—cultivate local “visiting atmospheres” through place imagery, perceived comfort, and commercial activity. Finally, nodal elements—such as signboards, temporary vendors, and public service facilities—produce multi-scale, site-centered effects that anchor and perturb flows and reinforce lingering, backtracking, and clustering at bridgeheads, squares, and comparable nodes. This study advances a shift from static and global description to a mechanism-oriented explanatory framework and clarifies the differentiated roles and linkages among environmental factors by integrating video-based trajectory analytics with machine learning interpretation. This framework demonstrates the applicability of surveillance and computer vision techniques for studying pedestrian dynamics in small-scale heritage settings, and offers practical guidance for heritage conservation and sustainable tourism management in similar historic environments. Full article

Cultural heritage conservation faces escalating challenges from environmental threats and resource constraints, necessitating innovative preservation strategies that balance predictive accuracy with interpretability. This study presents a hybrid neuro-fuzzy framework addressing critical gaps in heritage conservation practice through sequential integration of feedforward neural networks (FF-NNs) and Mamdani-type fuzzy inference systems (MFISs). The system processes multi-sensor data (temperature, vibration, pressure) through a two-stage architecture: an FF-NN for pattern recognition and an MFIS for interpretable decision-making. Evaluation on 1000 synthetic heritage building monitoring samples (70% training, 30% testing) demonstrates mean accuracy of 94.3% (±0.62%), precision of 92.3% (±0.78%), and recall of 90.3% (±0.70%) across five independent runs. Feature importance analysis reveals temperature as the dominant fault detection driver (60.6% variance contribution), followed by pressure (36.7%), while vibration contributes negatively (−2.8%). The hybrid architecture overcomes the accuracy–interpretability trade-off inherent in standalone approaches: while the FF-NN achieves superior fault detection, the MFIS provides transparent maintenance recommendations essential for conservation professional validation. However, comparative analysis reveals that rigid fuzzy rule structures constrain detection capabilities for borderline cases, reducing recall from 96% (standalone FF-NN) to 47% (hybrid system) in fault-dominant scenarios. This limitation highlights the need for adaptive fuzzy integration mechanisms in safety-critical heritage applications. Full article

Historic urban landscapes, despite their cultural significance, often face neglect, limiting their potential to increase the value of historical centers. Defined as a complex sociotechnical network that involves a variety of agencies incorporating material, immaterial, natural, and artificial elements, these landscapes present significant challenges for architects because of their layered and diverse components. Actor–network theory (ANT) is used as a methodological and ontological framework to address this complexity. However, a notable research gap exists on the basis of the lack of clear representation and practical application of ANT to address the complexity of these historic urban landscapes. To bridge this gap, this study uses Bey’s palace as a case study to develop a comprehensive framework based on a digital mapping approach rooted in ANT. This framework traces, visualizes, and analyzes historic urban landscapes as intricate systems of agencies, leveraging graph theoretical algorithms and computational analysis tasks from network analysis tools to increase their effectiveness. This investigation is based on two key concepts: the actor/actant and the actor network. The research employed Bruno Latour’s concepts of translation, agency, and the mapping controversies technique grounded in graph-theoretic algorithm tasks to decipher the complexities of Bey’s palace system. The results identify seven clusters as actor networks and highlight the roles of key actors/actants, such as Ahmed Bey, decorative elements, courtyard gardens, and Moorish architecture. This methodological approach provides architects and urban planners with practical tools to better understand, analyze and preserve historic urban landscapes, enriching their cultural and historical value. By transforming contested discourses into measurable networks indicators, this interdisciplinary framework directly supports SDG11 (Sustainable Cities and Communities), especially Target 11.4, in safeguarding cultural heritage by enabling the prioritization, monitoring and governance of cultural, social and infrastructural assets in historic urban landscapes. Full article

The pursuit of sustainable animal protein sources is critical in light of the environmental, social, and economic challenges associated with conventional livestock production. Although meat, including organic production, remains a valuable source of high-quality protein, diversification is essential to sustainably meet future demand. This review summarizes current knowledge on alternative animal protein sources, with a particular focus on insects and cultured meat in Europe. Insects demonstrate high feed conversion efficiency, require minimal land and water resources, and provide essential amino acids, lipids, and micronutrients, while contributing to circular economy models. Cultured meat presents potential advantages for environmental sustainability and animal welfare; however, its large-scale application depends on technological advances, cost reduction, and supportive regulation. Consumer acceptance remains a challenge influenced by cultural heritage, food neophobia, and product presentation. Policy frameworks, including the European Green Deal and the Farm to Fork Strategy, seek to foster innovation and sustainable food systems. Future perspectives emphasize that conventional and organic meat, insect-based protein, and cultured meat should be regarded as complementary solutions for a balanced and resilient protein supply in Europe. Full article

Historic Building Information Modelling (HBIM) is the application of BIM, an information management and modelling process, to cultural heritage (CH) assets. HBIM will provide tangible benefits to the heritage community by providing an enduring record of assets, facilitating more informed decision-making, and enabling more efficient resource management. However, the HBIM application is characterised by disparate methodologies and an inconclusive understanding of what the HBIM system should be achieving. The article aimed to validate thirty-three system requirements for HBIM previously proposed by the authors and evaluate their relative criticality for both the UK and international heritage community. It presents the results of an extensive survey undertaken with the international heritage community. The thirty-three system requirements were found to be valid for both the UK and international heritage community. However, some variation in the relative criticality of the requirements according to region was identified, the most notable of which was the perceived criticality of system requirements related to visualisation. Nine requirements were updated, and an additional requirement was added to reflect feedback received from participants. One requirement was altered by the authors to encompass a greater scope. Future work will investigate opportunities for requirement realisation and produce a theoretical framework for HBIM adoption. Full article

To address limitations such as cleaning difficulties or secondary contamination/damage of cultural relics caused by the uncontrollable diffusion of water/cleaning agent/dirty liquids during the cleaning process in traditional cleaning methods, this study, using cotton textiles as an example, systematically investigated the cleaning efficacy of four in situ methods (blank gel, cleaning gel, ultrasonic emulsification, and gel + ultrasonic emulsification synergistic cleaning) on eight types of stains, including sand, clay, rust, blood, ink, oil, and mixed solid/liquid stains. Building upon this, this study proposed an efficient, targeted, in situ, and controllable cleaning strategy tailored for fragile, stained textile relics. Results demonstrated that, regardless of the stain type, the synergistic cleaning method of G+U (gel poultice + ultrasonic emulsification) consistently outperformed the cleaning methods of blank gel poultice, cleaning gel poultice, and ultrasonic emulsification. Furthermore, the gel loaded with cleaning agents was always more effective than the blank gel (unloaded cleaning agents). The poultice methods of blank gel and cleaning gel were better suited for solid stains, while the ultrasonic emulsification cleaning method was more effective for liquid stains. Meanwhile, it was also found that the optimal cleaning method proposed in this study (the G+U synergistic cleaning method) was a cleaning method that restricted the cleaning agent within the gel network/emulsion system, and utilized the porous network physical structure of gel, the chemical action of emulsion’s wetting/dissolving dirt, and the cavitation synergistic effect of ultrasound to achieve the targeted removal of contaminants from relics’ surfaces. Crucially, the cleaning process of G+U also had the characteristics of controlling the cleaning area at the designated position and effectively regulating the diffusion rate of the cleaning solution within the treatment zone, as well as the reaction intensity. Therefore, the proposed optimal (the synergistic cleaning method of G+U) cleaning method conforms to the significant implementation of the “minimal intervention and maximal preservation” principle in modern cultural heritage conservation. Consequently, the synergistic cleaning method of G+U holds promise for practical application in artifact cleaning work. Full article

Artificial intelligence (AI) is profoundly transforming heritage tourism through the incorporation of technological solutions that reconfigure the ways in which cultural heritage is conserved, interpreted, and experienced. This article presents a critical and systematic review of current AI applications in this field, with a special focus on their impact on destination management, the personalization of tourist experiences, universal accessibility, and the preservation of both tangible and intangible assets. Based on an analysis of the scientific literature and international use cases, key technologies such as machine learning, computer vision, generative models, and recommendation systems are identified. These tools enable everything from the virtual reconstruction of historical sites to the development of intelligent cultural assistants and adaptive tours, improving the visitor experience and promoting inclusion. This study also examines the main ethical, technical, and epistemological challenges associated with this transformation, including algorithmic surveillance, data protection, interoperability between platforms, the digital divide, and the reconfiguration of heritage knowledge production processes. In conclusion, this study argues that AI, when implemented in accordance with principles of responsibility, sustainability, and cultural sensitivity, can serve as a strategic instrument for ensuring the accessibility, representativeness, and social relevance of cultural heritage in the digital age. However, its effective integration necessitates the development of sector-specific ethical frameworks, inclusive governance models, and sustainable technological implementation strategies that promote equity, community participation, and long-term viability. Furthermore, this article highlights the need for empirical research to assess the actual impact of these technologies and for the creation of indicators to evaluate their effectiveness, fairness, and contribution to the Sustainable Development Goals. Full article

Structured-light 3D reconstruction is an active measurement technique that extracts spatial geometric information of objects by projecting fringe patterns and analyzing their distortions. It has been widely applied in industrial inspection, cultural heritage digitization, virtual reality, and other related fields. This review presents a comprehensive analysis of mainstream fringe-based reconstruction methods, including Fringe Projection Profilometry (FPP) for diffuse surfaces and Phase Measuring Deflectometry (PMD) for specular surfaces. While existing reviews typically focus on individual techniques or specific applications, they often lack a systematic comparison between these two major approaches. In particular, the influence of different projection schemes such as Digital Light Processing (DLP) and MEMS scanning mirror–based laser scanning on system performance has not yet been fully clarified. To fill this gap, the review analyzes and compares FPP and PMD with respect to measurement principles, system implementation, calibration and modeling strategies, error control mechanisms, and integration with deep learning methods. Special focus is placed on the potential of MEMS projection technology in achieving lightweight and high-dynamic-range measurement scenarios, as well as the emerging role of deep learning in enhancing phase retrieval and 3D reconstruction accuracy. This review concludes by identifying key technical challenges and offering insights into future research directions in system modeling, intelligent reconstruction, and comprehensive performance evaluation. Full article

Landscapes are vital systems where ecological, cultural, perceptual, and socio-economic values meet, making their quality assessment essential for sustainable development. Landscape Quality (LQ), shaped by the interaction of natural processes and human activities, remains methodologically challenging due to its interdisciplinarity and the need to integrate multiple dimensions. This challenge is particularly perceived in peri-urban areas, predominantly understudied in landscape research. This article addresses this gap in LQ assessment at peri-urban landscapes, through the case of Houch Al Oumaraa, Zahleh, a peri-urban area of patrimonial significance and agricultural landscape value. To evaluate the four spatial dimensions of LQ (structural, ecological, cultural and visual), we adopted a mixed methodology, where a pre-developed set of landscape indicators (LIs) applied within GIS and spatial technics, were supplemented by expert analysis through visual studies. Two questions framed this research: (i) is remote sensing sufficient to assess peri-urban LQ, and (ii) what are the limits of applying pre-developed LIs to diverse landscape contexts? Results show moderate fragmentation (CONTAG 61.6%), low diversity (MSDI 0.27), high density of cultural monuments (PROTAP 4.19) and average visual disharmony (FCDHI 0.49). Findings reveal that spatial dimensions alone are insufficient for assessing LQ of peri-urban landscapes, where socio-economic dimensions must also be integrated. Structural indicators (PLAND, MPA, ED, CONTAG) and MSDI proved transferable, while ECOLBAR was less applicable, cultural indicators (PROTAP, HLE) were limited to tangible heritage, and visual indicators (FCDHI, SDHI) highly context dependent. Establishing a differentiated yet standardized framework would not only enhance methodological precision but also ensure that LQ assessment remain relevant across diverse contexts, providing policymakers with actionable insights to align planning with sustainability goals. Full article

Malay songket motifs are a vital component of Malaysia’s intangible cultural heritage, characterized by intricate visual designs and deep cultural symbolism. However, the practical digital preservation and retrieval of these motifs present challenges, particularly due to the rotational variations typical in textile imagery. This study introduces a novel Content-Based Image Retrieval (CBIR) model that integrates Principal Component Analysis (PCA) for feature extraction and Quadratic Geometric Distance (QGD) for measuring similarity. To evaluate the model’s performance, a curated dataset comprising 413 original images and 4956 synthetically rotated songket motif images was utilized. The retrieval system featured metadata-driven preprocessing, dimensionality reduction, and multi-angle similarity assessment to address the issue of rotational invariance comprehensively. Quantitative evaluations using precision, recall, and F-measure metrics demonstrated that the proposed PCAQGD + Rotation technique achieved a mean F-measure of 59.72%, surpassing four benchmark retrieval methods. These findings confirm the model’s capability to accurately retrieve relevant motifs across varying orientations, thus supporting cultural heritage preservation efforts. The integration of PCA and QGD techniques effectively narrows the semantic gap between machine perception and human interpretation of motif designs. Future research should focus on expanding motif datasets and incorporating deep learning approaches to enhance retrieval precision, scalability, and applicability within larger national heritage repositories. Full article

Thermal imaging is increasingly applied in remote sensing to identify material degradation, monitor structural integrity, and support energy diagnostics. However, its adoption is limited by the low spatial resolution of thermal sensors compared to RGB cameras. This study proposes a modular pipeline to generate thermally enriched 3D point clouds by fusing RGB and thermal imagery acquired simultaneously with a dual-sensor unmanned aerial vehicle system. The methodology includes geometric calibration of both cameras, image undistortion, cross-spectral feature matching, and projection of radiometric data onto the photogrammetric model through a computed homography. Thermal values are extracted using a custom parser and assigned to 3D points based on visibility masks and interpolation strategies. Calibration achieved 81.8% chessboard detection, yielding subpixel reprojection errors. Among twelve evaluated algorithms, LightGlue retained 99% of its matches and delivered a reprojection accuracy of 18.2% at 1 px, 65.1% at 3 px and 79% at 5 px. A case study on photovoltaic panels demonstrates the method’s capability to map thermal patterns with low temperature deviation from ground-truth data. Developed entirely in Python, the workflow integrates into Agisoft Metashape or other software. The proposed approach enables cost-effective, high-resolution thermal mapping with applications in civil engineering, cultural heritage conservation, and environmental monitoring applications. Full article

Polymers have long been central to modern materials science, but their durability has also made them major contributors to environmental pollution. A new generation of bio-based and nanostructured polymers is now reshaping this field, offering materials that are functional, reversible, and sustainable. This review examines their role across three interconnected domains: cultural heritage conservation, the protection of medicinal and aromatic plants (MAPs), and environmental sustainability. In heritage science, polymers are moving away from synthetic resins toward renewable systems such as chitosan, nanocellulose, and PLA, which provide stability while remaining reversible and compatible with delicate substrates. In agriculture, biodegradable coatings, controlled-release carriers, and edible films are improving MAP protection, extending shelf life, and reducing reliance on synthetic pesticides. In environmental applications, polymers are being reinvented as solutions rather than problems—through degradable mulches, functional hydrogels, and nanocomposites that clean soils and waters within a circular economy framework. Looking across these domains reveals strong synergies. The same principles—biodegradability, multifunctionality, and responsiveness—apply in each context, turning polymers from passive barriers into intelligent, adaptive systems. Their future success will depend not only on chemistry but also on life-cycle design, policy alignment, and public trust, making polymers key enablers of sustainability. Full article