[1] 201910-202210 大阪大学 博士研究生

[2] 201609-201906 东南大学 硕士研究生

[3] 201109-201606 南昌大学 大学本科

[1] 202210-* 南昌大学 南昌大学

基于类激活图可视化解释的街道视觉环境特征与步行性感知关联模型研究——以南昌市主城区社区街道为例

基于深度学习在复杂环境下室内人行为数据采集与分析技术开发

期刊论文

Li, Y., Yabuki, N., & Fukuda, T. (2023). Integrating GIS, deep learning, and environmental sensors for multicriteria evaluation of urban street walkability. Landscape and Urban Planning, 230, 104603. 

Li, Y., Yabuki, N., & Fukuda, T. (2022). Measuring visual walkability perception using panoramic street view images, virtual reality, and deep learning. Sustainable Cities and Society, 86, 104140. 

Li, Y., Yabuki, N., & Fukuda, T. (2022). Exploring the association between street built environment and street vitality using deep learning methods. Sustainable Cities and Society, 79, 103656. 

李韵琴，张嘉新，谢雨辰. 基于Grad-CAM的校园街道步行空间视觉感知体验研究[J]. 新建筑，2024（6）：18-23. 

Zhang, J., Xiang, R., Kuang, Z., Wang, B., & Li, Y.* (2024). ArchGPT: harnessing large language models for supporting renovation and conservation of traditional architectural heritage. Npj Heritage Science, 12(1), 1-14. 

Zhang, J., Li, Y.*, Fukuda, T., & Wang, B. (2025). Urban safety perception assessments via integrating multimodal large language models with street view images. Cities, 165, 106122.

Huang, D., Gong, W., Wang, X., Liu, S., Zhang, J., & Li, Y. (2025). A Cognition–Affect–Behavior Framework for Assessing Street Space Quality in Historic Cultural Districts and Its Impact on Tourist Experience. Buildings, 15(15), 2739. https://doi.org/10.3390/buildings15152739

Kuang, Z., Zhang, J.*, Li, Y., & Fukuda, T. (2025). Preserving architectural heritage in urban renewal: a stable diffusion model framework for automated historical facade generation. npj Heritage Science, 13(1), 1-19.

Li M, Zhu Z, Deng J, Zhang J, Li Y. (2025). Geospatial Explainable AI Uncovers Eco-Environmental Effects and Its Driving Mechanisms—Evidence from the Poyang Lake Region, China. Land. 2025; 14(7):1361.

Zhang, J., Hu, J., Zhang, X., Li, Y., & Huang, J. (2023). Towards a Fairer Green city: Measuring unfairness in daily accessible greenery in Chengdu’s central city. Journal of Asian Architecture and Building Engineering, 1–20.

Zhang, J., Yu, Z., Li, Y., & Wang, X. (2023). Uncovering Bias in Objective Mapping and Subjective Perception of Urban Building Functionality: A Machine Learning Approach to Urban Spatial Perception. Land, 12(7), 1322.

 Hu, J., Zhang, J., & Li, Y. (2022). Exploring the spatial and temporal driving mechanisms of landscape patterns on habitat quality in a city undergoing rapid urbanization based on GTWR and MGWR: The case of Nanjing, China. Ecological Indicators, 143, 109333. 

Xie, Y., Zhang, J., Li, Y., Zhu, Z., Deng, J., & Li, Z. (2024). Integrating multi-source urban data with interpretable machine learning for uncovering the multidimensional drivers of urban vitality. Land, 13(12), 2028.

 Ma, K., Wang, B., Li, Y., & Zhang, J. (2022). Image retrieval for local architectural heritage recommendation based on deep hashing. Buildings, 12(6), 809. 

 Wang, B., Zhang, J.*, Zhang, R., Li, Y., Li, L., & Nakashima, Y. (2024). Improving facade parsing with vision transformers and line integration. Advanced Engineering Informatics, 60, 102463.

Tang, Y., Zhang, J., Liu, R.*, & Li, Y. (2022). Exploring the Impact of Built Environment Attributes on Social Followings Using Social Media Data and Deep Learning. ISPRS International Journal of Geo-Information, 11(6), 325. 

Wan, R., Zhang, J.*, Huang, Y., Li, Y., Hu, B., & Wang, B. (2024). Leveraging Diffusion Modeling for Remote Sensing Change Detection in Built-Up Urban Areas. IEEE Access

Xu, S., Zhang, J.*, and Li, Y., Knowledge-Driven and Diffusion Model-Based Method for Generating Historical Building Facades: A Case Study of Traditional Minnan Residences in China, Information 2024, 15(6), 344.

Zheng, S.; Zhang, J.*; Zu, R.; Li, Y. Visual Perception Differences and Spatiotemporal Analysis in Commercialized Historic Streets Based on Mobile Eye Tracking: A Case Study in Nanchang Wanshou Palace, China. Buildings 2024, 14, 1899

Zhang, J., Huang, Y., Li, Z., Li, Y., Yu, Z., & Li, M.* (2024). Development of a Method for Commercial Style Transfer of Historical Architectural Facades Based on Stable Diffusion Models. Journal of Imaging, 10(7), 165.

Ma, Q., Zhang, J.*, & Li, Y. (2024). Advanced Integration of Urban Street Greenery and Pedestrian Flow: A Multidimensional Analysis in Chengdu’s Central Urban District. ISPRS International Journal of Geo-Information, 13(7), 254.

Zheng, S., Zhang, J.*, Zu, R., & Li, Y. (2024). Vision transformer-enhanced thermal anomaly detection in building facades through fusion of thermal and visible imagery. Journal of Asian Architecture and Building Engineering, 1-15.

Liang, H., Zhang, J., Li, Y., Wang, B., & Huang, J. (2024). Automatic Estimation for Visual Quality Changes of Street Space Via Street-View Images and Multimodal Large Language Models. IEEE Access.

国内/国际会议论文宣讲

Li, Y., Zhang, J., & Yu, C. (2019). Intelligent multi-objective optimization method for complex building layout based on pedestrian flow organization - a case study of people's court building in Anhui, China. The 24th Annual Conference of the Computer Aided Architectural Design Research Association of Asia (CAADRIA 2019), Wellington, New Zealand, April 2019.

Kuang, Z., Zhang, J., Huang, Y., & Li, Y. Advancing Urban Renewal: An Automated Approach to Generating Historical Arcade Facades with Stable Diffusion Models. HABITS OF THE ANTHROPOCENE - Proceedings of the 43rd ACADIA Conference - Volume II: Proceedings book one, University of Colorado Denver, Denver, Colorado, USA, 26-28 October 2023, pp. 616-625, CUMINCAD, 2023.

Kuang, Z., Zhang, J., Luo, X., Xie, Y. & Li, Y. (2024) Synthesizing User Preferences from Supplier Catalogs: A Large Multimodal Models Framework for Tailored Interior Design Solution. ACADIA 2024: Designing Change, University of Calgary, Calgary, Canada, 14-16 November 2024, CUMINCAD, 2024.

Li, Y., Yabuki, N., Fukuda, T., & Zhang, J. (2020). A big data evaluation of urban street walkability using deep learning and environmental sensors-a case study around Osaka University Suita campus. Proceedings of the 38th eCAADe conference, TU Berlin, Berlin, Germany (pp. 319-328). 

 Li, Y., Yabuki, N., & Fukuda, T. (2023). A Virtual Reality-Based Tool with Human Behavior Measurement and Analysis for Feedback Design of the Indoor Light Environment. CDRF2023, Hybrid Intelligence (pp. 187–196). Springer, Nature Singapore. 

Li, Y., Zhang, J., & Yu, C. (2019). Intelligent Multi-Objective Optimization Method for Complex Building Layout based on Pedestrian Flow Organization-A case study of People's Court building in Anhui, China. Proceedings of the Intelligent & Informed—The 24th CAADRIA Conference (Vol. 1, pp. 271-280). Victoria University of Wellington Wellington, New Zealand. 

郭超，李韵琴，张嘉新. 人本视角下基于自采集街景图像的景观性街道步行性评估方法—以南昌市为例[C]. 全国建筑院系建筑数字技术教学与研究学术研讨会，昆明，2024：中国建筑工业出版社.

Liao, S., Li, Y., & Zhang, J. Gender Differences in Visual Perception of Campus Pedestrian Spaces Based on Computer Vision Technology, CAADRIA 2025, University of Tokyo, Tokyo, Japan. March 25-29, 2025. 

Xie, Y., Li, Y., Zhang, J., & Zhang, J. Analysis of Differences in Street Visual Walkability Perception Between Dcnn and Vit Model Based on Panoramic Street View Images. CAADRIA 2024, Singapore University of Technology and Design, Singapore, Singapore. April 23-25, 2025.

Yuchen Xie, Yunqin Li, Jiaxin Zhang et al. (2024). What Is the Difference Between Images and Real-World Scenes in Street Visual Walkability Perception: A Case Study of a University Campus. In Proceedings of the 42nd Ecaade Conference, Nicosia, Cyprus.

 Li, Y., Yabuki, N., & Fukuda, T. (2022). A Virtual Reality-Based Tool with Human Behavior Measurement and Analysis for Feedback Design of the Indoor Light Environment. The 4th International Conference on Computational Design and Robotic Fabrication (pp. 187-196). Springer.