研究与教学方向
人工智能建筑学
建筑遗产保护与再生
空间感知
视觉智能
多源数据融合与大模型推理
虚拟现实
电子邮箱：liyunqin@ncu.edu.cn
教育简历
南昌大学建筑与工程学院，建筑学学士，2016年
东南大学建筑学院，建筑学硕士，2019年
大阪大学能源与环境学院，工学博士，2022年
学术及社会兼职
日本大阪大学客座研究员
南昌市社区规划师
顶级SCI、A&HCI期刊《Landscape and Urban Planning》、《Frontiers of Architecture Research》、《International Journal of Digital Earth》、《CITIES》等审稿人，国际顶级会议欧洲计算机辅助建筑设计会议（eCAADe）、亚洲计算机辅助建筑设计会议(CAADRIA)审稿人
课题
主持，江西省科技厅, 自然科学基金-青年基金项目, 20242BAB20223, 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, 2024-06 至 2026-05, 10万元, 在研
参与（排名第二），教育部, 人文社会科学研究-一般项目, 24YJAZH062, 武夷山区明清宗祠建筑营造谱系及传变研究,
2024-09 至 2027-08, 10万元, 在研
参与（排名第二），江西省教育厅，高效人文社科一般项目：赣中地区传统民居的形式语言解析与风貌保护数字化生成研究，JC24203
参与，江西省教育厅，基础研究项目，：本土建筑文化视角下的城市更新：传统与现代结合的实现路径研究（依托基地：南昌大学文化资源与产业研究院）
参与，2024年教育部产学合作协同育人项目：基于AIGC的“人-机-人”交互式建筑设计教学模式构建与优化研究，241004575314950
参与，江西省教育厅内地与港澳高等学校师生交流计划项目：“科技领航·筑梦家园”联合工作营
指导学生竞赛与科研项目
主持，科研训练项目：城市街道视觉特征与行人感知关联的量化模型研究——基于虚拟现实与深度学习技术，2023年
主持，科研训练项目：城市街道步行空间优化设计的眼动追踪诊断，2024年
参与，南昌大学大学生创新训练项目：“基于稳定扩散模型的传统历史街区智能化更新设计方法”，编号NCU-2024XC160
参与，南昌大学大学生创新训练项目：“赣脉智谱——赣系村落空间基因传承的践行者”，2024年
指导，东方设计奖全国高校创新设计大赛，省一等奖、省二等奖
指导，第21届 SuperMap杯高校GIS大赛，优胜奖
指导，“南昌市‘晾成美景’社区公共晒区设计方案竞赛”，二等奖
获奖
2024年“南粤杯”七校联合毕业设计竞赛 最佳指导老师
学院教师授课竞赛优秀奖，2024年
国家奖学金，2014-2015学年
2016年南昌大学“优秀毕业生”
大阪大学博士课程奖学金
2014年第五届高等院校“斯维尔”杯bim建模大赛建筑节能及日照分析专项一等奖(第五名)，建筑设计专项二等奖；建设工程VR仿真系统专项二等奖；团队全能二等奖等，共八个奖项
“江苏省研究生学术创新论坛（2017）——‘数字+’与变化的长三角”活动中佳作奖获得者
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., 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.