Application of Intelligent Technology in Facade Style Recognition of Harbin Modern Architecture

Shan, Linlin; Zhang, Long

doi:10.3390/su14127073

Cited by 8 publications

(7 citation statements)

References 27 publications

(30 reference statements)

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“…The facade elements that convey values that positively impact the characteristics of historic building groups should be identified [17]. Due to the differences in styles, materials, colors, and elements among facades [18,19], the complex and numerous types of historic building facades are always fragmented or incomplete [20,21]. In this case, verifying the universal values by a manual traversal procedure is an intricate and difficult process, especially when the building's facades are similar and only possess minor differences.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Visual Relationship between Function and Facade in Historic Buildings Using Deep Learning—A Case Study of the Chinese Eastern Railway

Li,

Zhao,

Zhang

et al. 2023

Sustainability

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Although functional identifiability represents a key aspect for promoting visual connotation and sustainable usability in historic building groups, there is still no consensus on how to quantitatively describe its identification basis at a large scale. The recent emergence of the potentiality of deep learning and computer vision has provided an alternative to traditional empirical-based judgment, which is limited by its subjective bias and high traversal costs. To address these challenges, this study aims to build a workflow for a visual analysis of function and facade to extract the different contributions that facade elements provide to functional expression. The approach is demonstrated with an experiment on a section of the Chinese Eastern Railway (CER) where large-scale historical buildings images were categorized to identify functions using deep learning, together with activation and substance for visual calculations. First, the dataset aggregated with images of historic buildings along the CER was used to identify functional categories using SE-DenseNet merging channel attention. The results of the model visualized using t-SNE and Grad-CAM were then used to analyze the relationships of facade features across functional categories and differences in elemental feature representation across functional prototypes. The results show the following: (1) SE-Densenet can more efficiently identify building functions from the closely linked facade images of historic building groups, with the average accuracy reaching 85.84%. (2) Urban–rural differences exist not only in the count of spatial distributions among the CER’s historic building groups, but also in a significant visual divergence between functions related to urban life and those involved in the military, industry, and railways. (3) Windows and walls occupy areas with more characteristics, but their decorative elements have a higher intensity of features. The findings could enhance the objective understanding and deeper characteristics of the historical building group system, contributing to integrated conservation and characteristic sustainability.

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Section: Introductionmentioning

confidence: 99%

Understanding the Visual Relationship between Function and Facade in Historic Buildings Using Deep Learning—A Case Study of the Chinese Eastern Railway

Li,

Zhao,

Zhang

et al. 2023

Sustainability

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“…The architectural style of traditional dwellings is a crucial component in managing the rural landscape and preserving cultural heritage structures [9,74]. Therefore, this study primarily focuses on establishing a dataset by collecting images of traditional dwellings within traditional villages in Gansu Province.…”

Section: Gansu Traditional Dwellings Dataset (Gtdd)mentioning

confidence: 99%

Classification and Model Explanation of Traditional Dwellings Based on Improved Swin Transformer

Miao,

Zhang,

Piao

et al. 2024

Buildings

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The extraction of features and classification of traditional dwellings plays significant roles in preserving and ensuring the sustainable development of these structures. Currently, challenges persist in subjective classification and the accuracy of feature extraction. This study focuses on traditional dwellings in Gansu Province, China, employing a novel model named Improved Swin Transformer. This model, based on the Swin Transformer and parallel grouped Convolutional Neural Networks (CNN) branches, aims to enhance the accuracy of feature extraction and classification precision. Furthermore, to validate the accuracy of feature extraction during the prediction process and foster trust in AI systems, explainability research was conducted using Grad-CAM-generated heatmaps. Initially, the Gansu Province Traditional Dwelling Dataset (GTDD) is established. On the constructed GTDD dataset, the Improved Swin Transformer attains an accuracy of 90.03% and an F1 score of 87.44%. Comparative analysis with ResNet-50, ResNeXt-50, and Swin Transformer highlights the outstanding performance of the improved model. The confusion matrix of the Improved Swin Transformer model reveals the classification results across different regions, indicating that the primary influencing factors are attributed to terrain, climate, and cultural aspects. Finally, using Grad-CAM-generated heatmaps for explaining classifications, it is observed that the Improved Swin Transformer model exhibits more accurate localization and focuses on features compared to the other three models. The model demonstrates exceptional feature extraction ability with minimal influence from the surrounding environment. Simultaneously, through the heatmaps generated by the Improved Swin Transformer for traditional residential areas in five regions of Gansu, it is evident that the model accurately extracts architectural features such as roofs, facades, materials, windows, etc. This validates the consistency of features extracted by the Improved Swin Transformer with traditional methods and enhances trust in the model and decision-making. In summary, the Improved Swin Transformer demonstrates outstanding feature extraction ability and accurate classification, providing valuable insights for the protection and style control of traditional residential areas.

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“…There are also research results on the application of new technological tools in the study of architectural facades. Shan adopts advanced image recognition algorithms to construct the CA-MSResNet model to capture the overall structural information of the architectural facade and the characteristics of the local components, and then accurately identifies the form elements and stylistic categories of Harbin buildings [17]. Cheng studies the spatial sequence of architectural elevations of traditional villages in Huizhou, South Anhui Province, uses 3D laser scanning technology to achieve the 3D modeling of architectural elevations, and then explores the regularity of the organization of architectural facades [18].…”

Section: Architectural-façade-related Researchmentioning

confidence: 99%

New Life in the Countryside: Conservation and Sustainability of Vernacular Architectural Facade Characteristics in the Jiangnan Region, China

Han,

Hu,

2024

Sustainability

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The facade form of vernacular architecture is an outward manifestation of the building structure, which can easily give people an intuitive impression of the building. Most of the existing studies focus on analyzing the construction process and building materials of vernacular architecture in detail, but there are few studies on the characteristic elements in the form of architectural facades. The main objective of this study is to propose a new methodology for objectively analyzing the morphological characteristics of architectural facades with complex networks to support the sustainable development of vernacular architecture. The results of the study show that the vernacular architectural facades in the Jiangnan region are characterized by the richness of details and distinctive layers and that the formal elements that can show the regional characteristics are preserved in the process of the sustainable development of vernacular architecture. The most crucial part of this is the roof shape, followed by the simplification of the detailing and how the vertical form elements are laid out horizontally. The article addresses the protection and continuity of vernacular architectural facade characteristics and proposes a strategic plan to strengthen the vernacular architectural facade characteristics in the Jiangnan region, aiming to protect the traditional characteristics and continue the traditional culture.

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Application of Intelligent Technology in Facade Style Recognition of Harbin Modern Architecture

Cited by 8 publications

References 27 publications

Understanding the Visual Relationship between Function and Facade in Historic Buildings Using Deep Learning—A Case Study of the Chinese Eastern Railway

Understanding the Visual Relationship between Function and Facade in Historic Buildings Using Deep Learning—A Case Study of the Chinese Eastern Railway

Classification and Model Explanation of Traditional Dwellings Based on Improved Swin Transformer

New Life in the Countryside: Conservation and Sustainability of Vernacular Architectural Facade Characteristics in the Jiangnan Region, China

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