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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)
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.
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.
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.
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.
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.
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.
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）
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）
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
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）
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.
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）
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）https://webplus.nju.edu.cn/_ueditor/themes/default/images/spacer.gif
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)https://webplus.nju.edu.cn/_ueditor/themes/default/images/spacer.gif
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)https://webplus.nju.edu.cn/_ueditor/themes/default/images/spacer.gif
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)https://webplus.nju.edu.cn/_ueditor/themes/default/images/spacer.gif
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)https://webplus.nju.edu.cn/_ueditor/themes/default/images/spacer.gif
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.https://webplus.nju.edu.cn/_ueditor/themes/default/images/spacer.gif
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. https://webplus.nju.edu.cn/_ueditor/themes/default/images/spacer.gif
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.https://webplus.nju.edu.cn/_ueditor/themes/default/images/spacer.gif
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.https://webplus.nju.edu.cn/_ueditor/themes/default/images/spacer.gif
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.https://webplus.nju.edu.cn/_ueditor/themes/default/images/spacer.gif
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.https://webplus.nju.edu.cn/_ueditor/themes/default/images/spacer.gif
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.https://webplus.nju.edu.cn/_ueditor/themes/default/images/spacer.gif
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.https://webplus.nju.edu.cn/_ueditor/themes/default/images/spacer.gif
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.https://webplus.nju.edu.cn/_ueditor/themes/default/images/spacer.gif
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.https://webplus.nju.edu.cn/_ueditor/themes/default/images/spacer.gif
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.https://webplus.nju.edu.cn/_ueditor/themes/default/images/spacer.gif
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