51吃瓜

学术贡献与影响

致力于遥感智能解译技术与应用创新研究，在面向对象遥感影像分析（OBIA）的误差传播理论，以及分类模型优化方面取得系列创新成果，开展了 OBIA在城市遥感领域的应用研究，并以局地气候分区（Local Climate Zones，LCZ）概念为切入点，搭建城市遥感与城市健康之间的研究桥梁，实现城市遥感制图成果的创新应用。主要研究成果包括：①针对OBIA理论认知不足，揭示了面向对象遥感分类过程中不同参数和方法影响分类的交互效应，构建了面向对象遥感监督分类误差传导理论体系，其被欧洲科学院院士Giles Foody誉为OBIA发展的标志性成果；②由于智能模型与对象范式融合难，提出了贯穿“分割-采样-特征-分类”全过程的面向对象遥感影像监督分类优化新模型，突破了人工智能技术在面向对象遥感影像分析范式中的集成瓶颈，出版《面向对象遥感影像分析理论与方法》中文专著；③以局地气候分区城市分类系统为桥梁，建立了精细空间下热健康风险研究框架，制备了中国城市LCZ样本库，并被全球城市数据库WUDAPT平台收录；同时，生产了中国城市局地气候分区大规模遥感高精度制图产品，入选中国地球观测优秀应用百佳案例；率先将局地气候分区概念推广应用至人口空间分布模拟、热健康风险分析等城市空间建模领域，实现了遥感科学、人工智能、城市治理等领域的交叉创新，系列成果在LCZ制图与应用建模方向产生广泛影响，小组三名成员（含两名硕士研究生）入选LCZ领域国际影响力学者。

代表论文与著作

[32]Yan, Z., Ma, L.*, Wang, X., Kim, Y., Zhang, L. High-precision population estimates by remote sensing big data and advanced transformer deep learning model. Remote Sensing Applications: Society and Environment, 2025, 39: 101638.

[31]Tan, L., Ma, L.*, Chen, C., Lu, H. Fusing multi-modal data for comprehensive quality evaluation of urban ecological space at grid scale: A case study in Taizhou, China. Sustainable Cities and Society, 2025, 130: 106527.

[30] Ma, L.*, Yan, Z., Li, M., Liu, T., Tan, L., Wang, X., He, W., Wang, R., He, G., Lu, H., Blaschke, T. Deep learning meets object-based image analysis: Tasks, challenges, strategies, and perspectives. IEEE Geoscience and Remote Sensing Magazine, 2025, 13(3): 136-163.

[29]Wang, R., Ma, L.*, He, G., Johnson, B. A., Yan, Z., Chang, M., Liang, Y. Transformers for remote sensing: A systematic review and analysis. Sensors, 2024, 24(11): 3495. (Invited and feature paper, free charge)

[28]Ma, L.*, Zhou, L., Blaschke, T., Yan, Z., He, W., Lu, H., Demuzere, M., Wang, X., Zhu, X., Zhang, L. Projecting high resolution population distribution using Local Climate Zones and multi-source big data. Remote Sensing Applications: Society and Environment, 2024, 33: 101077.

[27]马磊等, 深度学习在地学领域的应用进展与挑战. 科学观察, 2023. 18(06): 16-17. （中国地学研究热点论文特约稿）

[26]He, W., Ma, L.*, Yan, Z., Lu, H. Evaluation of advanced time series similarity measures for object-based cropland mapping. International Journal of Remote Sensing, 2023, 44 (12), 3777-3800.

[25]Ma, L.*, Yan, Z., He, W., Lv, L., He, G., Li, M.* Towards better exploiting object-based image analysis paradigm for local climate zones mapping. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 199, 73-86.

[24]Ma, L.*, Huang, G., Johnson, B.A., Chen, Z., Li, M., Yan, Z., Zhan, W., Lu, H., He, W., Lian, D. Investigating urban heat-related health risk based on local climate zones：A case study of Changzhou in Yangtze River Delta, China. Sustainable cities and society, 2023, 91, 104402. (ESI 高引)

[23]Zhou, L., Ma, L.*, Johnson，B.A., Yan, Z., Li, F., Li, M. Patch-Based Local Climate Zones Mapping and Population Distribution Pattern in Provincial Capital Cities of China. ISPRS international journal of geo-information, 2022. 11(420): 420.

[22]Yan, Z., Ma, L.*, He, W., Zhou, L., Lu, H., Liu, G., Huang, G. Comparing Object-Based and Pixel-Based Methods for Local Climate Zones Mapping with Multi-Source Data. Remote sensing, 2022. 14(3744): 3744.（Invited and feature paper, free charge）

[21]Ma, L., Yang, Z., Zhou, L., Lu, H., Yin, G. Local climate zones mapping using object-based image analysis and validation of its effectiveness through urban surface temperature analysis in China, Building and Environment , 2021, 206: 108348. （南大学科一流期刊）

[20]Ma, L., Zhu, X.,Qiu, C., Blaschke, T., Li, M. Advances of Local Climate Zone Mapping and Its Practice Using Object-Based Image Analysis, Atmosphere , 2021, 12: 1146

[19]马磊; 李满春; 程亮; 叶粟; 面向对象遥感影像分析理论与方法, 科学出版社, 350千字, 2020.（专著）

[18]Ma, L., Schmitt, M., Zhu, X.; Uncertainty Analysis of Object-Based Land-Cover Classification Using Sentinel-2 Time-Series Data, Remote sensing , 2020, 12(22): 3798.

[17]Johnson, B.A., Ma, L.*. Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities. Remote Sens. 2020, 12(11), 1772.（Editorial paper）

[16]Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G.,... Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166-177. （期刊Top 1高下载，ESI高引）

[15]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. (ESI 高引, 期刊Top 3高下载)

[14]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.(期刊高引)

[13]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.(ESI高引,期刊创刊以来十大高引， The Jack Dangermond Award –国际摄影测量与遥感协会 2017最佳论文)

[12]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, 2017年7/8月统计数据)

[11]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.

[10]Zhou, Z., Ma, L.*, Fu, T., Zhang, G., Yao, M.,... Li, M. (2018). Change Detection in Coral Reef Environment Using High-Resolution Images: Comparison of Object-Based and Pixel-Based Paradigms. ISPRS International Journal of Geo-Information, 7(11), 441.

[9]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.

[8]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.

[7]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.

[6]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.

[5]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.

[4]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.

[3]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.

[2]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.

[1]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.