[30]Ma, L., et al., Deep Learning Meets Object-Based Image Analysis: Tasks, challenges, strategies, and perspectives. IEEE Geoscience and Remote Sensing Magazine, 2024. 10.1109/MGRS.2024.3489952.
[29]Wang, R., Ma, L.*, et al., Transformers for remote sensing: a systematic review and analysis. Sensors, 2024, 24, 3495. (Invited and feature paper, free charge)
[28]Ma, L., et al., 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.
[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）
publication.pdf
[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)publication.pdf
[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)publication.pdf
[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)publication.pdf
[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)publication.pdf
[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.publication.pdf
[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. publication.pdf
[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.publication.pdf
[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.publication.pdf
[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.publication.pdf
[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.publication.pdf
[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.publication.pdf
[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.publication.pdf
[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.publication.pdf
[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.publication.pdf
[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.publication.pdf
