Deep Learning Model for Form Recognition and Structural Member Classification of East Asian Traditional Buildings

Ji, Seungyeul; Jun, Hanjong

doi:10.3390/su12135292

Cited by 7 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given their outstanding image classification and feature extraction capabilities, researchers have applied them to the field of architectural type recognition and classification, yielding commendable results [41]. Seung-Yeul et al [42] employed a Convolutional Neural Network (R-CNN) and a YOLO model to classify and locate architectural types and structural components in videos and images of East Asian traditional buildings from South Korea, Japan, and China. Gonzalez et al [43] utilized a Convolutional Neural Network (CNN) to annotate Google Street View photos, automatically identifying building materials and architectural structure types from building facades.…”

Section: Research On Machine Learning For Architectural Classificationmentioning

confidence: 99%

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

Miao,

Zhang,

Piao

et al. 2024

Buildings

View full text Add to dashboard Cite

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.

show abstract

Section: Research On Machine Learning For Architectural Classificationmentioning

confidence: 99%

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

Miao,

Zhang,

Piao

et al. 2024

Buildings

View full text Add to dashboard Cite

show abstract

“…Traditional BI methods rely on manually designed features (such as image element features, corner point features, spectral features, etc.) for extraction [3]. These methods are influenced by subjective factors, making it challenging to extract optimal features and imposing significant limitations.…”

mentioning

confidence: 99%

Cross-Scene Building Identification Based on Dual-Stream Neural Network and Efficient Channel Attention Mechanism

Li,

Zhang,

Xia

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

With the widespread popularity of deep learning, various neural network models are extensively employed in the recognition, classification, and segmentation of remote sensing images. Convolutional neural networks (CNNs), fully convolutional networks (FCNs), including their variants like Unet, have demonstrated significant results within this particular domain. Nevertheless, CNNs exhibit limitations when it comes to grasping extended global dependencies. Conversely, transformers exhibit exceptional ability in effectively dealing with long-range dependencies. Considering this, we have introduced the Efficient Channel Attention-enhanced Dual-stream Neural Network (ECA-DSNN) to improve the identification of buildings across various scenes. Specifically, we developed a dual-stream network that incorporates the Unet and transformer framework in order to capture both the local and global context. Additionally, we introduced an attention mechanism module to augment the model's generalization capability. With the advanced identification and generalization capability of ECA-DSNN, only fine-tuning and data augmentation are needed to achieve superior performance in cross-scene transfer, even with limited samples in the target domain. The outcomes indicated that the ECA-DSNN proposed achieved superior performance in comparison to the state-of-the-art (SOTA) methodologies, particularly in the experiment transferring from the source domain Beijing to the target domain Shanghai. In this scenario, the overall accuracy surpassed 96.3% and an F1 score exceeded 78.6%.

show abstract

Semantic object-based urban scene analysis for feature fusion of VHR imagery and Lidar DSM

Mahmoudi

2022

SIViP

View full text Add to dashboard Cite

Deep Learning Model for Form Recognition and Structural Member Classification of East Asian Traditional Buildings

Cited by 7 publications

References 10 publications

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

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

Cross-Scene Building Identification Based on Dual-Stream Neural Network and Efficient Channel Attention Mechanism

Semantic object-based urban scene analysis for feature fusion of VHR imagery and Lidar DSM

Contact Info

Product

Resources

About