AccScience Publishing / JCAU / Volume 6 / Issue 1 / DOI: 10.36922/jcau.1736
Cite this article
Journal Browser
Volume | Year
News and Announcements
View All

Exploring the spatial attributes of streets in Lu Xun’s hometown of Shaoxing, China, through image semantic segmentation

Qingyuan Hong1*
Show Less
1 Department of Architecture, School of Architecture, Southeast University, Nanjing, China
Journal of Chinese Architecture and Urbanism 2024, 6(1), 1736
Submitted: 31 August 2023 | Accepted: 25 October 2023 | Published: 5 January 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( )

Image semantic segmentation, a deep learning algorithm, enables the recognition of pixel collections that form distinct categories, allowing for the identification of vehicles, pedestrians, traffic signs, pavement, and other road features. In urban and architectural design domains, image semantic segmentation and related techniques empower practitioners and researchers to efficiently analyze the distribution of public spaces. This application facilitates a better understanding of how people interact with urban environments, ultimately improving the design of functional and inviting spaces. This paper presents an analysis of images of different streets within the Lu Xun Heritage Area in Shaoxing, Zhejiang Province, China, which were obtained through onsite photography. The images were sampled, segmented, and compared to assess the spatial characteristics of distinct street types. A self-trained semantic segmentation model based on the Cityscapes dataset and the PaddlePaddle framework was employed to statistically analyze space variations across various dimensions. This analysis contributes to a better understanding of historical street structure and provides insights into the integration of artificial intelligence in urban planning and design.

Lu Xun’s hometown
Semantic segmentation
Street space
Historic streets
Vibrant streets
Shaoxing China
This research received no financial support from public or private sectors.

An, J. (2021). Research on Intelligent Assistance Design of Subway Station Space Based on Deep Learning (Master’s Thesis, Beijing Jiaotong University). Available from: detail.aspx?dbname=cmfd202201&filename=1021868129.nh [Last accessed on 2023 Aug 24].


Cao, Y. (2017). Innovation and Extension of Urban Image Cognition Methods Based on Deep Learning. (Master’s Thesis, Chongqing University). Available from: aspx?dbname=cmfd201801&filename=1017838519.nh [Last accessed on 2023 Aug 24].


Cao, Y. (2021). Research and Implementation of Dataset Establishment and Detection Identification of Historical Building Components in Guangzhou. (Master’s Thesis, South China University of Technology). Available from: aspx?dbname=cmfd202301&filename=1021896319.nh [Last accessed on 2023 Aug 26].


Chen, F. C., & Jahanshahi, M. R. (2017). Vision-based Crack Detection on Metallic Surfaces using Deep Convolutional Neural Network with Patch Clustering: Conference: Structural Health Monitoring 2017.


Cheng, J. (2022). Nighttime Semantic Segmentation Based on Image Generation. (Master’s Thesis, Southeast University). Available from: aspx?dbname=CMFDTEMP&filename=1023530839.nh [Last accessed on 2023 Aug 24].


Deng, Q., Ou, H., Wang, Z., Li, Y., & Liu, Y. (2021). Two- Dimensional Image Recognition and Three-dimensional Stereoscopic Generation Research on General Layout Generated by GAN Model - A Case Study of Primary School Campus. In: Shaping the Future - Proceedings of the National Symposium on Architectural Digital Technology Teaching and Research in Architecture Institutes, 2021. China: Huazhong University of Science and Technology Press, p597-602.


Dong, B. (2022). Method and System Implementation for Architectural Floor Plan Structure Recognition Based on Multi-task Models. (Master’s Thesis, Zhejiang University). Available from: aspx?dbname=CMFD202301&filename=1022725650.nh [Last accessed on 2023 Aug 24].


Fan, Y. (2010). Development and Utilization of Humanistic Tourism Resources in Shaoxing. (Master’s Thesis, Zhejiang Normal University). Available from: aspx?dbname=cmfd2011&filename=2010241328.nh [Last accessed on 2023 Aug 26].


Fang, T., Zuo, J., & Gong, J. (2021). Research and application of computer vision in the field of construction engineering. Building Construction, 11:2376-2379, 2382.


Feng, J. (2020). Automatic Detection of Buildings in Remote Sensing Images Based on Multi-Scale Features. (Master’s Thesis, Wuhan University). Available from: aspx?dbname=cmfd202101&filename=1020970585.nh [Last accessed on 2023 Aug 26].


Gao, M., Zhang, H., Zhang, T., & Zhang, X. (2022). Spatial recognition of public building pixel construction drawings based on deep learning. Journal of Graphics, 2:189-196.


Gerhard, S., Xu, S., & Miao, Y. (2018). The second opportunity of artificial intelligence in architecture and urban design. Time and Architecture, 1:32-37.


Hu, T., Jie, P., Wen, Y., & Mu, H. (2023). A study of methods for extracting building outlines using different deep learning models. Remote Sensing Technology and Applications, 4:892-902.


Hui, D. (2021). Research on Strategies to Enhance Street Vitality in Xi’an Shuyuanmen Area Based on Image Recognition. [Master’s Thesis, Xi’an University of Architecture and Technology]. Available from: aspx?dbname=cmfd202201&filename=1021819297.nh [Last accessed on 2023 Aug 24].


Lee, B. J., Shin, D. H., Seo, J. W., Jung, J. D., & Lee, J. Y. (2011). Intelligent Bridge Inspection Using Remote Controlled Robot and Image Processing Technique. In: 28th International Symposium on Automation and Robotics in Construction (ISARC 2011). Seoul, Korea: International Association for Automation and Robotics in Construction (IAARC), p1426-1431.


Liu, Y. (2021). Possibilities of architectural design in the intelligent era. New Industrialization, 9:181-182, 184.


Liu, Y., Zhou, J., Qi, W., Li, X., Gross, L., Shao, Q., Zhao, Z., Ni, L., Fan, X., & Li, Z. (2022). ARC-Net: An efficient network for building extraction from high-resolution aerial images. IEEE Access, 8:154997-155010.


Picon, A., & Zhou, J. (2019). How are humans doing? Artificial intelligence in architecture. Time and Architecture, 6:14-19.


Shi, C. (2022). Research on Building Extraction Based on Deep Learning. (Master’s Thesis, Beijing University of Civil Engineering and Architecture). Available from: aspx?dbname=cmfd202301&filename=1022566693.nh [Last accessed on 2023 Aug 26].


Shuai, N. (2022). Research on Building Extraction and Change Detection of Remote Sensing Images Based on Semantic Segmentation. [Master’s Thesis, Southeast University]. Available from: aspx?dbname=cmfdtemp&filename=1023531802.nh [Last accessed on 2023 Aug 24].


Wang, D. (2022). Deformation Monitoring of Ancient Buildings Based on Computer Vision Methods. [Master’s Thesis, Beijing Jiaotong University]. Available from: aspx?dbname=cmfd202302&filename=1022820429.nh [Last accessed on 2023 Aug 24].


Wang, J. (2022). Research on Street Green Space Evaluation Based on Multi-Source Big Data. (Master’s Thesis, Shenyang Jianzhu University). Available from: detail.aspx?dbname=cmfd202301&filename=1022839113. nh [Last accessed on 2023 Aug 26].


Wang, N. (2019). Application Research of Deep Learning in Surface Damage Detection of Ancient Buildings. (Doctoral Dissertation, Dalian University of Technology). Available from: aspx?dbname=cdfdlast2020&filename=1019243428.nh [Last accessed on 2023 Aug 24].


Wang, P., Xiao, J., Duan, Z., & Li, C. (2022). Development trends of intelligent building facade damage detection. Journal of Architecture and Civil Engineering, 4:24-37.


Wang, Y. (2020). Research on Image Semantic Segmentation Algorithm Based on Deep Learning. (Master’s Thesis, Xi’an University of Architecture and Technology). Available from: aspx?dbname=cmfd202101&filename=1020379330.nh [Last accessed on 2023 Aug 26].


Yang, C. (2022). Urban Spatial Expansion Identification and Driving Analysis Based on Deep Learning and SHAP Explanation. (Master’s Thesis, Wuhan University). Available from: aspx?dbname=cmfdtemp&filename=1022553223.nh [Last accessed on 2023 Aug 26].


Yang, J., & Zhu, X. (2021). Exploring gradual interactive design patterns of AI urban design at the block scale. Urban Planning International, 2:7-15.


Zeng, X., Chen, S., & Yang, Y. (2021). An Analysis of the Application of Generative Adversarial Networks in the Field of Architectural Design. In: Shaping the Future - Proceedings of the National Symposium on Architectural Digital Technology Teaching and Research in Architecture Institutes in 2021. Bristol, UK: Huazhong University of Science and Technology Press, pp10-16.


Zhang, G., Dong, J., Qin, J., & Li, W. (2021). Research on energy-saving technology of high-speed rail station lighting system based on artificial intelligence. Manufacturing Automation, 12:127-130, 147.


Zhou, J., Liu, Y., Nie, G., Cheng, H., Yang, X., Chen, X., & Gross, L. (2022). Building extraction and floor area estimation at the village level in rural China via a comprehensive method integrating UAV photogrammetry and the novel EDSANet. Remote Sensing, 14(20):5175.

Conflict of interest
The author declares no competing interests in this paper.
Back to top
Journal of Chinese Architecture and Urbanism, Electronic ISSN: 2717-5626 Published by AccScience Publishing