Smart City Gnosys
Smart city article details
| Title | The Classification Method Of Urban Architectural Styles Based On Deep Learning And Street View Imagery |
|---|---|
| ID_Doc | 55087 |
| Authors | Sun H.; Xu H.; Wei Q. |
| Year | 2022 |
| Published | Advances in Transdisciplinary Engineering, 31 |
| DOI | http://dx.doi.org/10.3233/ATDE220940 |
| Abstract | The task of identifying urban architectural styles occupies a very necessary position in the fields of construction of smart cities, sustainable urban development and community regeneration. The research method proposed in this paper can improve on the inconveniences of traditional methods of identifying urban architectural styles, such as: the community building is relatively old, and the integration of more periods of architectural style can significantly affect the test results. It is an established fact that data cannot be collected and processed efficiently by humans alone, and can not enter such qualitative and descriptive research methods into the computer for auxiliary research. This paper is based on the explosion of information data use in the 21st century, and use deep learning technology to process unstructured data with convolutional neural networks as the core to assist in the identification of urban architectural styles. With the rapid development of deep learning technology in recent years, its classification techniques for identification of street images of urban buildings can be used for urban management, and a new strong underpinning for the allocation of urban resources, urban diversification management, and the transformation of old communities in the later period has been provided by the proper classification of urban architectural styles. Notwithstanding its restrictions, the approach presented in this research has shown promise and the valuable value of deep learning-based techniques for the study of architectural styles, and this approach has universal significance. © 2022 The authors and IOS Press. |
| Author Keywords | deep learning; Style recognition; urban planning |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
4137  | 0.873 | Yang M.; Zhao L.; Ye L.; Jiang H.; Yang Z. | A Review Of Convolutional Neural Networks Related Methods For Building Extraction From Remote Sensing Images; [基于卷积神经网络的遥感影像建筑物提取方法综述] | Journal of Geo-Information Science, 26, 6 (2024) |
| 14125 | 0.867 | Zhuang Y.; Guo C. | City Architectural Color Recognition Based On Deep Learning And Pattern Recognition | Applied Sciences (Switzerland), 13, 20 (2023) |
| 48265 | 0.861 | Arulananth T.S.; Kuppusamy P.G.; Ayyasamy R.K.; Alhashmi S.M.; Mahalakshmi M.; Vasanth K.; Chinnasamy P. | Semantic Segmentation Of Urban Environments: Leveraging U-Net Deep Learning Model For Cityscape Image Analysis | PLoS ONE, 19, 4 April (2024) |
| 13116 | 0.861 | Zhang S.; Li M.; Zhao W.; Wang X.; Wu Q. | Building Type Classification Using Cnn-Transformer Cross-Encoder Adaptive Learning From Very High Resolution Satellite Images | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18 (2025) |
| 45402 | 0.86 | Liu X.; Meng F.; Chen L. | Research On Green Building Optimization Design Of Smart City Based On Deep Learning | World Automation Congress Proceedings, 2022-October (2022) |
| 13602 | 0.858 | Akhavi Zadegan A.; Vivet D.; Hadachi A. | Challenges And Advancements In Image-Based 3D Reconstruction Of Large-Scale Urban Environments: A Review Of Deep Learning And Classical Methods | Frontiers in Computer Science, 7 (2025) |
| 5029 | 0.853 | Mercioni M.A.; Holban S. | A Study On Hierarchical Clustering And The Distance Metrics For Identifying Architectural Styles | Proceedings of 2019 International Conference on ENERGY and ENVIRONMENT, CIEM 2019 (2019) |
| 45896 | 0.853 | Lv B.; Peng L.; Wu T.; Chen R. | Research On Urban Building Extraction Method Based On Deep Learning Convolutional Neural Network | IOP Conference Series: Earth and Environmental Science, 502, 1 (2020) |
| 1337 | 0.852 | Treccani D.; Balado J.; Fernández A.; Adami A.; Díaz-Vilariño L. | A Deep Learning Approach For The Recognition Of Urban Ground Pavements In Historical Sites | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 43, B4-2022 (2022) |