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The article discusses the potential of advanced technologies in the field of cultural heritage. It highlights how these technologies, such as virtual reality, artificial intelligence, and 3D printing, can be used to understand, preserve, and enhance cultural heritage. The article also presents scientific papers from the Florence Heri-Tech International Conference, showcasing the various applications of these technologies. The papers cover topics such as the use of hyperspectral imaging for hieroglyph recognition, the enhancement of user experience in cultural spaces through advanced systems, and the use of non-invasive techniques for conservation. Overall, the article emphasizes the significant impact of technology on the research, preservation, and promotion of cultural heritage. [Extracted from the article]

As immovable stone cultural heritage is kept in the open air, they are more susceptible to damage, and damage detection is very important for the protection and restoration of cultural heritage. This is especially true for gold-overlaid stone cultural heritage, which is usually more complicated than ordinary stone carvings. However, the detection of cultural heritage damages is mainly based on expert visual inspection, which is often subjective, time-consuming, and laborious. This paper uses the Mask R-CNN algorithm to rapidly and accurately detect the gold foil shedding of stone cultural heritage through two-dimensional images. The research data are from the high-precision images of the Dazu Thousand-Hand Bodhisattva Statue (World Heritage, UNESCO) in Chongqing, China. After cleaning and augmentation, 1900 images are input into Mask R-CNN model for training. Finally, the average precision value (AP) for detecting gold foil shedding is found to be 0.967. In order to test the performance of the model, the new images that do not participate in the training period are used, and it is found that the model can still accurately detect the gold foil shedding even if there are interference factors. This is the first attempt to detect the damages of gold-overlaid stone cultural heritage based on a deep learning algorithm, and it has achieved good results. [ABSTRACT FROM AUTHOR]

Anchor and fissure grouting are used to repair earthen sites. However, the common method to obtain the compressive strength of grouting slurry would cause material, labor, and time losses. In addition the material properties, environmental and economic benefits have gained increasing attention. This study proposes a design framework for multi-objective proportioning optimization based on machine learning and metaheuristics. The results indicated that the eXtreme Gradient Boosting (XGBoost) model, whose hyper-parameters were optimized by a genetic algorithm, can accurately predicted the compressive strength of the slurries. The impact of the variables on development of compressive strength can explain the internal reaction mechanisms. The analytical framework based on meta-heuristic and technique for order of preference by similarity to an ideal solution (TOPSIS) provided Pareto-optimal solutions in design scenario of each sub-dataset. The framework proposed in this study can efficiently achieve mechanical, environmental, and economic design objectives of anchor grouting and fissure grouting slurries for repairing earthen sites. [ABSTRACT FROM AUTHOR]

With the rapid development of machine learning technology, it has become possible to automatically identify cultural heritage elements in traditional buildings. This research aimed to develop a machine learning model based on the YOLOv4 architecture to identify the traditional Chinese porcelain inlay pattern in the Lingnan region. The researchers collected and annotated a large quantity of Lingnan Chinese porcelain inlay image data and then used these data to train the studied model. The research results show that (1) the model in this study was specifically adjusted to effectively identify a variety of Chinese porcelain inlay pattern types, including traditional patterns such as plum blossoms and camellias. (2) In the 116th epoch, the model showed excellent generalization ability, and the verification loss reached the lowest value of 0.88. The lowest training loss in the 195th epoch was 0.99, indicating that the model reached an optimal balance point for both recognition accuracy and processing speed. (3) By comparing different models for detecting Chinese porcelain inlay images across 581 pictures, our YOLOv4 model demonstrated greater accuracy in most classification tasks than did the YOLOv8 model, especially in the classification of chrysanthemums, where it achieved an accuracy rate of 87.5%, significantly outperforming YOLOv8 by 58.82%. However, the study also revealed that under certain conditions, such as detecting apples and pears in low-light environments, YOLOv8 showed a lower missing data rate, highlighting the limitations of our model in dealing with complex detection conditions. [ABSTRACT FROM AUTHOR]

This study aims to promote the protection and inheritance of cultural heritage in private gardens in the Jiangnan area of China. By establishing a precise visual labeling system and accelerating the construction of a database for private garden features, we deepen the understanding of garden design philosophy. To this end, we propose an improved Jiangnan private garden recognition model based on You Only Look Once (YOLO) v8. This model is particularly suitable for processing garden environments with characteristics such as single or complex structures, rich depth of field, and cluttered targets, effectively enhancing the accuracy and efficiency of object recognition. This design integrates the Diverse Branch Block (DBB), Bidirectional Feature Pyramid Network (BiFPN), and Dynamic Head modules (DyHead) to optimize model accuracy, feature fusion, and object detection representational capability, respectively. The enhancements elevated the model's accuracy by 8.7%, achieving a mean average precision (mAP@0.5) value of 57.1%. A specialized dataset, comprising 4890 images and encapsulating various angles and lighting conditions of Jiangnan private gardens, was constructed to realize this. Following manual annotation and the application of diverse data augmentation strategies, the dataset bolsters the generalization and robustness of the model. Experimental outcomes reveal that, compared to its predecessor, the improved model has witnessed increments of 15.16%, 3.25%, and 11.88% in precision, mAP0.5, and mAP0.5:0.95 metrics, respectively, demonstrating exemplary performance in the accuracy and real-time recognition of garden target elements. This research not only furnishes robust technical support for the digitization and intelligent research of Jiangnan private gardens but also provides a potent methodological reference for object detection and classification research in analogous domains. [ABSTRACT FROM AUTHOR]

This review presents the computational method of topic modeling to identify core topics and time trends in research on X-ray fluorescence (XRF) and its application to cultural heritage. Topic modeling is an approach to text mining based on unsupervised machine learning, which helps to determine core topics within a vast body of text. Due to the large amount of published work on X-ray fluorescence in the area of cultural heritage, traditional literature review has become impractical, inefficient, time-consuming, and potentially less reliable. Therefore, it is important to take stock of which topics have been core to such research and whether specific time trends can be identified within them. Using topic modeling, this review aims to reveal core topics and trends in research on XRF analysis of painted heritage objects by examining 982 articles collected from Web of Science. Within this dataset of articles, ten topics have been identified. The identified topics can be clustered in three main categories: the methods used, the objects studied, and the specific materials studied. In terms of trends in topic share since 2010, it is especially noteworthy to see that the share of articles focused on the identification and study of painting materials and techniques has more than doubled. Similarly, another impressive increase can be observed for articles centered on advanced imaging spectroscopic techniques, such as macro X-ray fluorescence (MA-XRF) and reflectance hyperspectral imaging, for the study of easel paintings. The share of attention within XRF literature given to imaging spectroscopic techniques tripled between 2010 and 2017, though stabilizing in the subsequent years. Conversely, the share of articles which specifically deal with the development and improvement of energy dispersive X-ray fluorescence (ED-XRF) spectroscopic techniques (i.e., portable ED-XRF, confocal micro-XRF, micro-grazing exit XRF) for the elemental analysis (including elemental depth profiling) of painted heritage objects has declined sharply. [ABSTRACT FROM AUTHOR]