个人简介
Hey 你好！我是何鹏飞，中国矿业大学测绘工程专业学士、地图制图学与地理信息工程工学博士，现任南京信息工程大学遥感与测绘工程学院讲师，测绘工程系教工党支部书记。
<关注的事情>
长期以来，我关注如何利用多源遥感数据开展区域/全球地表要素的动态监测。面向未来，重点关注地理空间智能方法及其在城市、生态场景下的行业应用与知识发现。
<讲授的课程>
曾讲授GNSS原理与应用、数字摄影测量学、大地测量学，现主讲本科生课程遥感原理与应用、遥感数字图像处理、摄影测量学、GIS设计与开发、地图叙事：空间表达的科学与艺术（公选）及研究生课程测量数据处理。
授课风格：注重教学内容及方式创新，注重促进学生对知识逻辑的拆解和梳理，注重促进学生动手实践。
<一些荣誉>
2025年遥感与测绘工程学院师德先进个人
2024、2025 年全国大学生测绘学科创新创业智能大赛——测绘技能竞赛特等奖一项、二等奖一项（优秀指导教师）
2024、2025年江苏省大学生测绘学科创新创业智能大赛特等奖一项，一等奖一项、二等奖一项（优秀指导教师）
2024年全国高等学校测绘学科教学创新与育才能力大赛——教学创新大赛二等奖 2022年第十一届全国高等学校测绘类专业青年教师讲课竞赛——摄影测量与遥感课程一等奖
2022年首届全国高等学校测绘类专业教师教学创新大赛一等奖
2022年江苏省高校测绘地理信息本科生优秀毕业论二等奖
2021年、2023年南京信息工程大学优秀班主任
2021年第二届南京信息工程大学教师教学创新竞赛二等奖
2020年遥感与测绘工程学院“勇于担当”奖
<欢迎一起>
诚挚欢迎真诚而好学的本科生、硕士研究生加入我们，
我乐于：
① 对具体问题的持续钻研并保有耐心；
② 与你在“做”的过程中一起成长；
③ 为你的学习生活提供必要的物质和心理支持；
④ 鼓励在专注高效的工作和健康多彩的生活间寻求平衡（工作+运动）。
期待你是真诚而负责的，好奇而进取的，踏实而勇毅的，欢迎联络。
以上。
更多内容，详见——
Education
• Doctoral Degree in Cartography and Geographical Information Engineering Sep. 2013 – Dec. 2017
China University of Mining and Technology, Xuzhou, China
Research on change detection algorithms based on remote sensing uncertainty analysis
• Bachelor's Degree in Surveying and Mapping Engineering Sep. 2008 – Jun. 2012
China University of Mining and Technology, Xuzhou, China
Fundamental theories and skills of Geo-spatial data collection and processing
Employment
• Lecturer Jan. 2018 – Present
Nanjing University of Information Science and Technology (NUIST), Nanjing, China
Engaging in teaching and research at the School of Surveying and Geomatics Engineering, NUIST.
• Research Assistant Aug. 2014 – Jan. 2015 & Dec. 2015 – Jun. 2016
The Hong Kong Polytechnic University (PolyU), Hong Kong, China
Research stays at the Department of Land Surveying and Geo-Informatics, PolyU.
Interests
Regional/global land surface feature detection and dynamic monitoring based on multi-source geospatial data and intelligent algorithms
Chaired Projects
• Research on high-resolution remotely sensed image change detection based on visual mechanisms, funded by the Natural Science Foundation of Jiangsu Province (BK20190785), Jul. 2019 – Jun. 2023.
• Super-pixel-based unsupervised change detection, funded by the Fundamental Research Funds for the Central Universities of China (2017BSCXB39), Jan. 2017 – Jan. 2018.
Awards
• Second Prize in the National Teaching Innovation Competition for Teachers Majoring in Surveying and Mapping, Aug. 2024.
• Excellent Head-Teacher, NUIST, Dec. 2023.
• First Prize in the National Lecture Competition for Young Teachers Majoring in Surveying and Mapping, Jul. 2022.
• First Prize in the National Teaching Innovation Competition for Teachers Majoring in Surveying and Mapping, Jul. 2022.
• Second Prize in the Teaching Innovation Competition at NUIST, Sep. 2021.
• Excellent Head-Teacher, NUIST, Dec. 2021.
Publications
• Yang, F., He, P., Wang, H., Hou, D., Li, D., and Shi, Y. Long-term, high-resolution GPP mapping in Qinghai using multi-source data and google earth engine. International Journal of Digital Earth, 2023; 16(2), 4885- 4905. https://doi.org/10.1080/17538947.2023.2288131
• Yang, F., He, P., Ding, H., and Shi, Y. A monthly high-resolution net primary productivity dataset (30 m) of Qinghai Plateau from 1987 to 2021. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023; 16, 8262-8273. https://doi.org/10.1109/JSTARS.2023.3312518
• He, P., Shi, Y., Ding, H., and Yang, F. Classification and transition of grassland in Qinghai, China, from 1986 to 2020 with Landsat archives on Google Earth Engine. Land, 2023; 12(9), 1686. https://doi.org/10.3390/land12091686
• Shi M., He P., Shi Y. Detecting extratropical cyclones of the northern hemisphere with Single Shot Detector. Remote Sensing, 2022; 14(2):254. https://doi.org/10.3390/rs14020254
• Peng, D., Bruzzone, L., Zhang, Y., Guan, H. and He, P. SCDNET: A novel convolutional network for semantic change detection in high resolution optical remote sensing imagery. International Journal of Applied Earth Observation and Geoinformation, 2021;103:102465. https://doi.org/10.1016/j.jag.2021.102465
• He, P., Zhao, X., Shi, Y., Cai, L. Unsupervised change detection from remotely sensed images based on multi-scale visual saliency coarse-to-fine fusion. Remote Sensing. 2021;13, 630. https://doi.org/10.3390/rs13040630
• He, P., Shi, W., & Zhang, H. Adaptive superpixel based Markov random field model for unsupervised change detection using remotely sensed images. Remote Sensing Letters, 2018; 9(8), 724- 732. https://doi.org/10.1080/2150704X.2018.1470698
• Hao, M., Shi, W., Deng, K., Zhang, H. and He, P., 2016. An object-based change detection approach using uncertainty analysis for VHR images. Journal of Sensors, 2016.https://doi.org/10.1155/2016/9078364
• Shao, P.; Shi, W.; He, P.; Hao, M.; Zhang, X. Novel approach to unsupervised change detection based on a robust semi-supervised FCM clustering algorithm. Remote Sensing. 2016, 8,264. https://doi.org/10.3390/rs8030264
• Shi, W., Shao, P., Hao, M., He, P. and Wang, J. Fuzzy topology–based method for unsupervised change detection. Remote Sensing Letters, 2016; 7(1), 81-90. https://doi.org/10.1080/2150704X.2015.1109155
• He, P., Shi, W., Miao Z. Zhang, H., Can L. Advanced Markov Random field model based on local uncertainty for unsupervised change detection. Remote Sensing Letters, 2015; 6(9), 667- 676. https://doi.org/10.1080/2150704X.2015.1054045
• Cai, L., Shi, W., He, P., Miao Z., Hao, M., Zhang, H., Can L. Fusion of multiple features to produce a segmentation algorithm for remote sensing images. Remote Sensing Letters, 2015; 6(5), 390- 398.https://doi.org/10.1080/2150704X.2015.1037467
• He, P., Shi, W., Zhang, H., Hao M. A novel dynamic threshold method for unsupervised change detection from remotely sensed images. Remote Sensing Letters, 2014; 5(4), 396- 403. https://doi.org/10.1080/2150704X.2014.912766
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