马磊

发布者:庞宝鑫发布时间:2018-05-02浏览次数:196

个人简介

马磊,男,博士,南京大学地理与海洋科学学院特任副研究员,德国洪堡学者(Humboldt Research Fellow),注册测绘师。主要从事面向对象高分遥感图像处理、中分遥感时间序列分析、GIS应用等研究。在从事的高分遥感图像处理研究领域,重点关注面向对象遥感影像分析技术,率先在国际上撰写并发表了关于面向对象遥感影像监督分类的综述论文,阐述了面向对象监督分类理论体系,系统论证了面向对象遥感影像分析的不确定性机制。主持或参与美国地质调查局地表覆盖连续变化监测项目(USGS-NASA Landsat Science Team Program)、国家自然科学基金青年科学基金项目、江苏省青年基金项目、中国博士后科学基金、国家公派研究生项目等国家和部省级多个项目。已发表SCI论文20篇,其中110篇、通作4篇、ESI高引论文1篇(1%)、高下载论文1篇(遥感1Top期刊)。发表论文被引290次,单篇最高被引51次,H因子11。申请或授权国家发明专利9件、登记软件著作权5项。担任国际期刊《Remote Sensing》客座编辑,受邀为《Remote Sensing of Environment》、《Remote Sensing》、《IEEE JSARS》、《European Journal of Remote Sensing》、《International Journal of Digital Earth》等10余种国际期刊审稿。获德国洪堡奖学金、江苏省优秀博士学位论文等奖励。

主要经历

2018.01 -,  南京大学,地理与海洋科学学院,特任副研究员(专职系列)

2016.06 -  2017.12,  南京大学,地理与海洋科学学院,助理研究员

2014.12 -  2015.12,  萨尔茨堡大学(奥地利),地理系,联合培养博士

2012.07 -  2016.06,  南京大学,地理与海洋科学学院,博士

2011.06 -  2012.06,  德阳市建设局,工程师

2008.09 -  2011.06,  西南交通大学,硕士

2004.09 -  2008.06,  西南交通大学,本科

科研项目

2017-2021,国家重点研发项目子课题,南海情势推演与决策支持系统,主持

2018-2020,国家自然科学基金项目,面向对象高分遥感影像分类的不确定性及模型优化研究,主持

2017-2020,江苏省青年基金项目,基于机器学习的面向对象遥感影像分类方法研究,主持

2017-2018,中国博士后科学基金特别资助项目,南海岛礁面向对象遥感变化监测研究,主持

2016-2018,中国博士后科学基金面上资助项目(一等资助),面向对象遥感影像分析的不确定性研究,主持

2017-2018,江苏省博士后资助项目,面向对象高分遥感影像分析范式研究,主持

2017-2017,中央高校基本科研业务费项目,南京大学国家自然科学基金培育项目,主持

获奖情况

2018,第一届“中国高分杯”智慧旅游挑战大赛(二等奖)(2/5

2018,德国洪堡奖学金(1/1

2017,江苏省优秀博士学位论文(1/1

2016,“南京大学优秀博士研究生创新能力提升计划”优秀项目(1/1

2013,博士研究生国家奖学金(1/1

2012,西南交通大学优秀硕士论文(1/1

发表论文

[1] Ma, L., Li, M. C., Ma, X. X. (2017): A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 277-293. (期刊高下载)

[2] Ma, L., Cheng, L., Li, M. C., Liu, Y., Ma, X. X. (2015): Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 102, 14-27.

[3] Li, M. C., Ma, L.*, Blaschke, T., Cheng, L., Tiede, D. (2016): A systematic comparison of different object-based classification techniques using high spatial resolution imagery. International Journal of Applied Earth Observation and Geoinformation, 49, 87-98. (ESI 高引, 20177/8月统计数据)

[4] Ma, L., Fu, T. Y., Li, M. C. (2018): Active learning for object-based image classification using predefined training objects. International Journal of Remote Sensing, 39:9, 2746-2765.

[5] Ma, L., Li, M. C., Blaschke, T., Ma, X. X., Tiede, D., Cheng, L., Chen, Z. J., Chen, D. (2016): Object-Based Change Detection in urban areas: the effects of segmentation strategy, scale, and feature space on unsupervised methods. Remote Sensing, 8(9), 761.

[6] Ma, L., Gao, Y., Fu, T., Cheng, L., Chen, Z., Li, M. (2017): Estimation of Ground PM2.5 Concentrations using a DEM-assisted Information Diffusion Algorithm: A Case Study in China. Scientific Reports, 7, 15556.

[7] Ma, L., Li, M. C., Gao, Y., Chen, T., Ma, X. X., Qu, L. A. (2017): A novel wrapper approach for feature selection in object-based image classification using ppolygon-based cross-validation. IEEE Geoscience and Remote Sensing Letters, 14(3), 409-413.

[8] Ma, L., Cheng, L., Han, W. Q., Zhong, L. S., Li, M. C. (2014): Cultivated land information extraction from high-resolution unmanned aerial vehicle imagery data. Journal of Applied Remote Sensing, 8, 1-25.

[9] Ma, L., Li, Y. S., Liang, L., Li, M. C., Cheng, L. (2013): A novel method of quantitative risk assessment based on grid difference of pipeline sections. Safety Science, 59, 219-226.

[10] Ma, L., Cheng, L., Li, M. C. (2013): Quantitative risk analysis of urban natural gas pipeline networks using geographical information systems. Journal of Loss Prevention in the Process Industries, 26, 1183-1192.

[11] Ma, L., Fu, T. Y., Blaschke, T., Li, M. C., Tiede, D., Zhou, Z. J., Ma, X. X., Chen, D. (2017): Evaluation of feature selection methods for object-based land cover mapping of Unmanned Aerial Vehicle imagery using Random Forest and Support Vector Machine classifiers. ISPRS International Journal of Geo-Information, 6(2), 51/1-51/22.

[12] Fu, T., Ma, L.*, Li, M. C., Johnson, B. A. (2018): Using convolutional neural network to identify irregular segmentation objects from very high-resolution remote sensing imagery. Journal of Applied Remote Sensing, 12(2), 025010.

[13] Gao, Y., Ma, L.*, Liu, J. X., Zhuang, Z. Z., Huang, Q. H., Li, M. C. (2017): Constructing Ecological Networks Based on Habitat Quality Assessment: A Case Study of Changzhou, China. Scientific Reports, 7, 46073.

[14] Cheng, L., Li, S., Ma, L.*, Li, M. C., Ma, X. X. (2015): Fire spread simulation using GIS: Aiming at urban natural gas pipeline. Safety Science, 75, 23-35.

[15] Cheng, L., Ma, L., Yang, K., Liu, Y. X., Li, M. C. (2013): Registration of Mars remote sensing images under the crater constraint. Planetary and Space Science, 2013, 85, 13-23.

[16] Cheng, L., Ma, L., Cai, W. T., Tong, L. H., Li, M. C., Du, P. J. (2013): Integration of Hyperspectral imagery and sparse sonar data for shallow water bathymetry mapping. IEEE Transactions on Geoscience and Remote Sensing, 2014, 53(6), 3235-3249.

[17] Ma, X. X., Wang, L. C., Ma, L., Zhang, Y. Q. (2015): Effects on sediments following water–sediment regulations in the Lixia River watershed, China. Quaternary International, 2015, 380/381, 334-341.

[18] Ma, X. X., Wang, L. C., Wu, H., Li, N., Ma, L., Zeng, C. F., Zhou, Y., Yang, J. (2015): Impact of Yangtze river water transfer on the water quality of the Lixia river watershed, China. Plos One, 10, e119720.

[19] Liu, Y., Hu, C., Zhan, W., Sun, C., Murch, B., Ma, L. (2018): Identifying industrial heat sources using time-series of the VIIRS Nightfire product with an object-oriented approach. Remote Sensing of Environment, 204, 347-365.

[20] Cheng, L., Yuan, Y., Xia, N., Chen, S., Chen, Y., Yang, K., Ma, L., Li, M. C. (2018): Crowd-sourced pictures geo-localization method based on street view images and 3D reconstruction. ISPRS Journal of Photogrammetry and Remote Sensing 141, 72-85.

联系方式

通讯地址:南京市仙林大道163号,邮编 210023

电子邮件:maleinju@nju.edu.cn