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Large-scale classification of architectural styles in Chinese traditional settlements is important for heritage conservation and geospatial documentation, but scalable deployment remains constrained by the high cost of expert annotation because villages are widely distributed, the imagery is captured from heterogeneous viewpoints, and each architectural tradition exhibits substantial intra-class variation. To address this bottleneck, we propose CTSMatch, a label-efficient semi-supervised framework that combines an ImageNet-pretrained EfficientNetV2 backbone with SoftMatch-based adaptive pseudo-label weighting so that ambiguous but informative unlabeled samples can still contribute to training, thereby reducing reliance on costly expert annotation. We also construct SemiCTS, an extension of the original CTS dataset that adds 4360 unlabeled images. Using only 545 labeled samples, CTSMatch achieves 96.93% accuracy on SemiCTS, outperforming the strongest fully supervised baseline (Dense-TL-Aug) by 2.73 percentage points and two standard semi-supervised baselines (FixMatch and FreeMatch) by 3.06 percentage points. Beyond classification, we further analyze the feature space to examine stylistic lineage through intra-style heterogeneity, inter-style transitions, and outlier detection. The results reveal two broad regional groupings, a northern cluster (Jing, Jin, Su) and a southern cluster (Chuan, Min, Wan), connected by gradual transitions rather than rigid boundaries. Approximately 15% of the samples are identified as atypical cases, including 8.7% comprising regional variants and 6.3% comprising hybrid forms. These findings show that CTSMatch provides a practical label-efficient framework for architectural heritage classification while supporting the interpretable analysis of stylistic diversification and convergence in Chinese traditional settlements.

The classification of the images taken during the measurement of an architectural asset is an essential task within the digital documentation of cultural heritage. A large number of images are usually handled, so their classification is a tedious task (and therefore prone to errors) and habitually consumes a lot of time. The availability of automatic techniques to facilitate these sorting tasks would improve an important part of the digital documentation process. In addition, a correct classification of the available images allows better management and more efficient searches through specific terms, thus helping in the tasks of studying and interpreting the heritage asset in question. The main objective of this article is the application of techniques based on deep learning for the classification of images of architectural heritage, specifically through the use of convolutional neural networks. For this, the utility of training these networks from scratch or only fine tuning pre-trained networks is evaluated. All this has been applied to classifying elements of interest in images of buildings with architectural heritage value. As no datasets of this type, suitable for network training, have been located, a new dataset has been created and made available to the public. Promising results have been obtained in terms of accuracy and it is considered that the application of these techniques can contribute significantly to the digital documentation of architectural heritage.

This paper set out an automatic multicategory damage detection technique using convolutional neural networks (CNN) models based on image classification and features’ extraction, to detect damages of historic structures such as: erosion, material loss, color change of the stone, and sabotage issues. The city of “Al-Salt” in Jordan was selected for the case study in this research. The best model showed an average damage detection accuracy of 95%. It was demonstrated that the proposed CNN model was significantly powerful, effective and reliable for damage detection of historic masonry buildings using features’ extraction based on imaging, and it contributed to the management and safety of historic heritage and preservation.

The recent years saw an extensive use of 3D point cloud data for heritage documentation, valorisation and visualisation. Although rich in metric quality, these 3D data lack structured information such as semantics and hierarchy between parts. In this context, the introduction of point cloud classification methods can play an essential role for better data usage, model definition, analysis and conservation. The paper aims to extend a machine learning (ML) classification method with a multi-level and multi-resolution (MLMR) approach. The proposed MLMR approach improves the learning process and optimises 3D classification results through a hierarchical concept. The MLMR procedure is tested and evaluated on two large-scale and complex datasets: the Pomposa Abbey (Italy) and the Milan Cathedral (Italy). Classification results show the reliability and replicability of the developed method, allowing the identification of the necessary architectural classes at each geometric resolution.

Architectural heritage assessment increasingly relies on automated visual analysis, yet existing deep learning approaches often lack interpretability and provide limited insight into how cultural value judgments are formed. To address this gap, this study proposes an interpretable multi-task framework—YOLOv11-CVHP—for architectural heritage image recognition and integrated value classification. The model incorporates a lightweight backbone network (RepGhostNet), an enhanced attention module (ArchDetectAttn), and the WIoU loss function to improve detection accuracy and robustness. Based on the architectural components and semantic attributes detected by YOLOv11-CVHP, seven visual–cultural variables were constructed to quantify heritage characteristics. A Random Forest classifier was then applied to predict four-level integrated value grades. Although Random Forest is commonly regarded as a black-box model, interpretability is achieved through the incorporation of SHAP, which attributes the contribution of each visual–cultural feature to the final value grade, allowing transparent analysis of the decision process. Results indicate that Cultural Value (Intellectual) consistently serves as the dominant discriminative factor across all levels, while Historical Period and Structural Integrity play critical roles in differentiating between higher value categories. The classifier demonstrates strong generalization, with five-fold Precision–Recall curves showing stable performance and ROC–AUC scores exceeding 0.90 on both training and test sets. In conclusion, the integrated YOLOv11-CVHP and SHAP-enhanced Random Forest framework provides both high accuracy and clear interpretability, offering a practical and explainable solution for automated architectural heritage identification and value assessment.

The classification of architectural style for Chinese traditional settlements (CTSs) has become a crucial task for developing and preserving settlements. Traditionally, the classification of CTSs primarily relies on manual work, which is inefficient and time consuming. Inspired by the tremendous success of deep learning (DL), some recent studies attempted to apply DL networks such as convolution neural networks (CNNs) to achieve automated classification of the architecture styles. However, these studies suffer overfitting problems of the CNNs, leading to inferior classification performance. Moreover, most of the studies apply the CNNs as a black box providing limited interpretability. To address these limitations, a new DL classification framework is proposed in this study to overcome the overfitting problem by transfer learning and learning-based data augmentation technique (i.e., AutoAugment). Furthermore, we also employ class activation map (CAM) visualization technique to help understand how the CNN classifiers work to abstract patterns from the input. Specifically, due to a lack of architectural style datasets for the CTSs, a new annotated dataset is first established with six representative classes. Second, several representative CNNs are leveraged to benchmark the new dataset. Third, to address the overfitting problem of the CNNs, a new DL framework is proposed which combines transfer learning and AutoAugment to improve the classification performance. Extensive experiments are conducted on the new dataset to demonstrate the effectiveness of our framework. The proposed framework achieves much better performance than baselines, greatly mitigating the overfitting problem. Additionally, the CAM visualization technique is harnessed to explain what and how the CNN classifiers implicitly learn for recognizing a specified architectural style.

In recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms based on machine and deep learning methods allow to process huge amounts of data as 3D point clouds. In this context, the aim of this paper is to make a comparison between machine and deep learning methods for large 3D cultural heritage classification. Then, considering the best performances of both techniques, it proposes an architecture named DGCNN-Mod+3Dfeat that combines the positive aspects and advantages of these two methodologies for semantic segmentation of cultural heritage point clouds. To demonstrate the validity of our idea, several experiments from the ArCH benchmark are reported and commented.

The use of machine learning techniques for point cloud classification has been investigated extensively in the last decade in the geospatial community, while in the cultural heritage field it has only recently started to be explored. The high complexity and heterogeneity of 3D heritage data, the diversity of the possible scenarios, and the different classification purposes that each case study might present, makes it difficult to realise a large training dataset for learning purposes. An important practical issue that has not been explored yet, is the application of a single machine learning model across large and different architectural datasets. This paper tackles this issue presenting a methodology able to successfully generalise to unseen scenarios a random forest model trained on a specific dataset. This is achieved looking for the best features suitable to identify the classes of interest (e.g., wall, windows, roof and columns).

As an important sustainable architecture, vernacular architecture plays a significant role in influencing both regional architecture and contemporary architecture. Vernacular architecture is the traditional and natural way of building that involves necessary changes and continuous adjustments. The formal characteristics of vernacular architecture are accumulated in the process of sustainable development. However, most of the research methods on vernacular architecture and its formal features are mainly based on qualitative analysis. It is therefore necessary to complement this with scientific and quantitative means. Based on the object detection technique, this paper proposes a quantitative model that can effectively recognize and detect the formal features of architecture. First, the Chinese traditional architecture image dataset (CTAID) is constructed, and the model is trained. Each image has the formal features of “deep eave”, “zheng wen”, “gable” and “long window” marked by experts. Then, to accurately identify the formal features of vernacular architecture in Jiangsu Province, the Jiangsu traditional vernacular architecture image dataset (JTVAID) is created as the object dataset. This dataset contains images of vernacular architecture from three different regions: northern, central, and southern Jiangsu. After that, the object dataset is used to predict the architectural characteristics of different regions in Jiangsu Province. Combined with the test results, it can be seen that there are differences in the architectural characteristics of the northern, middle, and southern Jiangsu. Among them, the “deep eave”, “zheng wen”, “gable”, and “long window” features of the vernacular architecture in southern Jiangsu are very outstanding. Compared with middle Jiangsu, northern Jiangsu has obvious features of “zheng wen” and “gable”, with recognition rates of 45.8% and 27.5%, respectively. The features of “deep eave” and “long windows” are more prominent in middle Jiangsu, with recognition rates of 50.9% and 73.5%, respectively. In addition, architectural images of contemporary vernacular architecture practice projects in the Jiangsu region are selected and they are inputted into the AOD R-CNN model proposed in this paper. The results obtained can effectively identify the feature style of Jiangsu vernacular architecture. The deep-learning-based approach proposed in this study can be used to identify vernacular architecture form features. It can also be used as an effective method for assessing territorial features in the sustainable development of vernacular architecture.

Thanks to its capability of archiving and organizing all the information about a building, HBIM (Historical Building Information Modeling) is considered a promising resource for planned conservation of historical assets. However, its usage remains limited and scarcely adopted by the subjects in charge of conservation, mainly because of its rather complex 3D modeling requirements and a lack of shared regulatory references and guidelines as far as semantic data are concerned. In this study, we developed an HBIM methodology to support documentation, management, and planned conservation of historic buildings, with particular focus on non-geometric information: organized and coordinated storage and management of historical data, easy analysis and query, time management, flexibility, user-friendliness, and information sharing. The system is based on a standalone specific-designed database linked to the 3D model of the asset, built with BIM software, and it is highly adaptable to different assets. The database is accessible both with a developed desktop application, which acts as a plug-in for the BIM software, and through a web interface, implemented to ensure data sharing and easy usability by skilled and unskilled users. The paper describes in detail the implemented system, passing by semantic breaking down of the building, database design, as well as system architecture and capabilities. Two case studies, the Cathedral of Parma and Ducal Palace of Mantua (Italy), are then presented to show the results of the system’s application.