姓名:张锴
职称: 青年研究员  硕士生导师 
性别:男
毕业院校:南京师范大学
学历:博士研究生
学位:理学博士
在职信息:在职
所在单位:资源环境学院
入职时间:2026
办公地点:祁连堂225
电子邮箱:zhk@lzu.edu.cn
学习经历
2019.09-2023.12,南京师范大学 地理科学学院,地图学与地理信息系统,博士(导师:陈旻教授) 2022.01-2022.12,瑞典梅拉达伦大学,能源 GIS,联合培养博士生(合作导师:严晋跃院士) 2016.09-2019.07,陕西师范大学 地理科学与旅游学院,地图学与地理信息系统,硕士(导师:薛亮副教授) 2012.09-2016.07,九江学院 旅游与国土资源学院,资源环境与城乡规划管理,学士
研究方向
能源GIS;光伏气候效应
工作经历
2026.03-至今,兰州大学 资源环境学院,青年研究员 2024.08-2025.10,香港中文大学 地理与资源管理学系,博士后研究员(合作导师:徐袁副教授)
学术兼职
1. 2025.01-至今,亚洲地理学会青年地理学家工作组,秘书; 2. 2021.05-2024.12,亚洲地理学会青年地理学家工作组,办公室助理; 3. 担任Information Geography、Springer Nature Computer Science、Annals of GIS等国际期刊审稿人。
研究成果
1. 系统量化中国高速公路光伏开发潜力,明确其能源利用价值与应用潜力边界; 2. 构建基于深度学习算法与街景影像数据的道路噪音屏障识别框架,实现噪音屏障的精准、高效识别; 3. 建立城市分布式光伏系统潜力评估及经济可行性分析框架,适配城市多元空间场景,为城市光伏资源化利用提供技术支撑与决策依据。
获得荣誉
1. 中国国土经济学会2023年度十大科技进展(4/14),2024.04 2. 优秀共享开放遥感数据集2022年度“十大最具价值年度数据集”(5/12),2023.08 3. 南京师范大学 2020-2021 年度优秀研究生,2021.12
在研项目
兰州大学“双一流”人才队伍建设科研项目,2026.03-2031.02,100万,主持(在研)
发表论文
第一或通讯作者论文(通讯作者*,共同第一作者#) [6] Zhang, K., Ma, P. and Xu, Y.*, 2026. Scaling Solar in Dense Cities: The Availability and Affordability of Distributed Photovoltaics (DSPV) Electricity in Shenzhen. Renewable Energy, p.125376. (SCI一区 TOP) [5] Zhang, K., Wang, D., Chen, M.*, Zhu, R., Zhang, F., Zhong, T., Qian, Z., Wang, Y., Li, H., Yang, Y., Lü, G. and Yan, J., 2024. Power generation assessment of photovoltaic noise barriers across 52 major Chinese cities. Applied Energy, 361, p.122839(SCI一区 TOP) [4] Zhang, K., Chen, M.*, Zhu, R., Zhang, F., Zhong, T., Lin, J., You, L., Lü, G., Yan, J., 2024. Integrating photovoltaic noise barriers and electric vehicle charging stations for sustainable city transportation. Sustainable Cities and Society, 100 (22), 104996. (SCI一区 TOP0) [3] Zhang, K., Chen, M.*, Yang, Y., Zhong, T., Zhu, R., Zhang, F., Qian, Z., Lü, G. and Yan, J., 2022. Quantifying the photovoltaic potential of highways in China. Applied Energy, 324, p.119600.(SCI一区 TOP) [2] Zhang, K., Qian, Z., Yang, Y., Chen, M.*, Zhong, T., Zhu, R., Lv, G. and Yan, J., 2022. Using street view images to identify road noise barriers with ensemble classification model and geospatial analysis. Sustainable cities and society, 78, p.103598.(SCI一区 TOP) [1] Zhong, T#., Zhang, K#., Chen, M.*, Wang, Y., Zhu, R., Zhang, Z., Zhou, Z., Qian, Z., Lv, G. and Yan, J., 2021. Assessment of solar photovoltaic potentials on urban noise barriers using street-view imagery. Renewable Energy, 168, pp.181-194. (SCI一区 TOP) 合作作者论文 [8] 陈旻*, 张锴, 朱瑞. 基于遥感与GIS技术的太阳能光伏潜力评估[J]. 遥感学报, 2025. [7] Ma, Z., Li, H., Zhang, K., Wang, J., Yue, S., Wen, Y., Lü, G., & Chen, M. 2025. Knowledge co-creation during urban simulation computation to enable broader participation. Sustainable Cities and Society, 118, 105994. [6] Zhang, Z., Chen, M., Zhong, T., Zhu, Qian, Z., R., Zhang, F. Yang, Y., Zhang, K., Paolo S., Wang, K., Pu, Y., Tian, L., Lv, G. and Yan, J., 2023. Carbon mitigation potential afforded by rooftop photovoltaic in China. Nature Communications 14, 2347. [5] Zhang, Z., Qian, Z., Zhong, T., Chen, M., Zhang, K., Yang, Y., Zhu, R., Zhang, F., Zhang, H., Zhou, F. and Yu, J., 2022. Vectorized rooftop area data for 90 cities in China. Scientific Data, 9(1), p.66. [4] Qian, Z., Chen, M., Yang, Y., Zhong, T., Zhang, F., Zhu, R., Zhang, K., Zhang, Z., Sun, Z., Ma, P. and Lü, G., 2022. Vectorized dataset of roadside noise barriers in China using street view imagery. Earth System Science Data, 14(9), pp.4057-4076. [3] Qian, Z., Chen, M., Zhong, T., Zhang, F., Zhu, R., Zhang, Z., Zhang, K., Sun, Z. and Lü, G., 2022. Deep Roof Refiner: A detail-oriented deep learning network for refined delineation of roof structure lines using satellite imagery. International Journal of Applied Earth Observation and Geoinformation, 107, p.102680.(SCI收录) [2] Zhong, T., Zhang, Z., Chen, M., Zhang, K., Zhou, Z., Zhu, R., Wang, Y., Lü, G. and Yan, J., 2021. A city-scale estimation of rooftop solar photovoltaic potential based on deep learning. Applied Energy, 298, p.117132. [1] Zhu, R., Anselin, L., Batty, M., Kwan, M.P., Chen, M., Luo, W., Cheng, T., Lim, C.K., Santi, P., Cheng, Zhang, K., C. and Ratti, C., 2021. The effects of different travel modes and travel destinations on COVID-19 transmission in global cities. Science Bulletin, 67(6), p.588.




