个人简介
何鹏飞,中国矿业大学地图制图学与地理信息工程工学博士,南京信息工程大学遥感与测绘工程学院副教授,硕士生导师,测绘工程系教工党支部书记。
<研究方向>
遥感解译、地理空间智能、城市遥感(欢迎品行端正、好学力行的本科生、硕士研究生加入我们!)
<教育工作经历>
2018-1 至今 南京信息工程大学 专任教师
2015-12 至 2016-1 香港理工大学 土地测量与地理空间科学系 Research Assistant
2014-8 至 2015-1 香港理工大学 土地测量与地理空间科学系 Research Assistant
2012-9 至 2017-12 中国矿业大学 地图制图学与地理信息工程 工学博士
2008-6 至 2012-12 中国矿业大学 测绘工程 工学学士
<讲授课程>
曾讲授本科生课程GNSS原理与应用、数字摄影测量学、大地测量学、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。
<教学与人才培养>
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
<科学研究>
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.
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
Awards
2017年中国测绘地理信息学会测绘科技进步特等奖,序17
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