Dongping Ming

2.3k total citations
101 papers, 1.8k citations indexed

About

Dongping Ming is a scholar working on Media Technology, Atmospheric Science and Ocean Engineering. According to data from OpenAlex, Dongping Ming has authored 101 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 57 papers in Media Technology, 46 papers in Atmospheric Science and 19 papers in Ocean Engineering. Recurrent topics in Dongping Ming's work include Remote-Sensing Image Classification (56 papers), Remote Sensing and Land Use (43 papers) and Automated Road and Building Extraction (17 papers). Dongping Ming is often cited by papers focused on Remote-Sensing Image Classification (56 papers), Remote Sensing and Land Use (43 papers) and Automated Road and Building Extraction (17 papers). Dongping Ming collaborates with scholars based in China, United States and Hong Kong. Dongping Ming's co-authors include Xianwei Lv, Yangyang Chen, Keqi Zhou, Min Wang, Hanqing Bao, Lu Xu, Jiancheng Luo, Jonathan Li, Min Zhang and Junyi Wang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Remote Sensing of Environment and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Dongping Ming

92 papers receiving 1.7k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Dongping Ming China	23	866	533	529	429	395	101	1.8k
Jiancheng Luo China	23	533 0.6×	409 0.8×	593 1.1×	502 1.2×	533 1.3×	131	1.7k
Isabel Sargent United Kingdom	9	695 0.8×	335 0.6×	360 0.7×	428 1.0×	333 0.8×	19	1.3k
Elif Sertel Türkiye	27	652 0.8×	483 0.9×	791 1.5×	737 1.7×	911 2.3×	101	2.5k
Zhanlong Chen China	24	611 0.7×	303 0.6×	349 0.7×	168 0.4×	470 1.2×	79	1.9k
Manfred Ehlers Germany	22	939 1.1×	275 0.5×	298 0.6×	487 1.1×	371 0.9×	73	2.0k
Andy Gardiner United Kingdom	9	599 0.7×	318 0.6×	322 0.6×	362 0.8×	270 0.7×	15	1.3k
B. Guindon Canada	19	720 0.8×	398 0.7×	727 1.4×	929 2.2×	699 1.8×	66	2.1k
Wei Han China	27	806 0.9×	421 0.8×	339 0.6×	477 1.1×	310 0.8×	106	2.2k
Chunping Qiu China	20	658 0.8×	417 0.8×	529 1.0×	322 0.8×	550 1.4×	66	1.6k
Mohammad Javad Valadan Zoej Iran	23	709 0.8×	377 0.7×	255 0.5×	279 0.7×	833 2.1×	152	2.2k

Countries citing papers authored by Dongping Ming

Since Specialization

Citations

This map shows the geographic impact of Dongping Ming's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dongping Ming with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dongping Ming more than expected).

Fields of papers citing papers by Dongping Ming

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dongping Ming. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dongping Ming. The network helps show where Dongping Ming may publish in the future.

Co-authorship network of co-authors of Dongping Ming

This figure shows the co-authorship network connecting the top 25 collaborators of Dongping Ming. A scholar is included among the top collaborators of Dongping Ming based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dongping Ming. Dongping Ming is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Xiao, Ling, Dongping Ming, Zhi Zhang, et al.. (2025). Revealing the causal response in landslide hydrology with MT-InSAR and spatial-temporal CCM: A case study in Jinsha River. Environmental Modelling & Software. 188. 106434–106434. 2 indexed citations

Li, Miao, et al.. (2025). SFEARNet: A Network Combining Semantic Flow and Edge-Aware Refinement for Highly Efficient Remote Sensing Image Change Detection. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–18. 1 indexed citations

Ming, Dongping, et al.. (2025). Change detection of slow-moving landslide with multi-source SBAS-InSAR and Light-U2Net. International Journal of Applied Earth Observation and Geoinformation. 136. 104387–104387. 3 indexed citations

Lv, Xianwei, et al.. (2025). Deep Merge: Deep-Learning-Based Region Merging for Remote Sensing Image Segmentation. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–20.

Ming, Dongping, et al.. (2025). A Hybrid Damaged Building Sample Generation Method Based on Cross-Scale Fusion Generative Model for Destroyed Building Detection After Earthquake. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–15.

Zhang, Liang, et al.. (2024). A new slope unit extraction method based on terrain topology searching and vector similarity constraint for landslide analysis. CATENA. 246. 108355–108355. 1 indexed citations

Lv, Xianwei, Claudio Persello, Wufan Zhao, et al.. (2023). Pruning for image segmentation: Improving computational efficiency for large-scale remote sensing applications. ISPRS Journal of Photogrammetry and Remote Sensing. 202. 13–29. 4 indexed citations

Ming, Dongping, et al.. (2023). A Stepwise Framework for Fine-Scale Mining Area Types Recognition in Large-Scale Scenes by GF-5 and GF-2 Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 16. 5714–5727. 6 indexed citations

Chen, Yangyang, Dongping Ming, Junchuan Yu, et al.. (2022). Susceptibility-Guided Landslide Detection Using Fully Convolutional Neural Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 16. 998–1018. 20 indexed citations

10.

Lv, Xianwei, et al.. (2021). BTS: a binary tree sampling strategy for object identification based on deep learning. International Journal of Geographical Information Systems. 36(4). 822–848. 4 indexed citations

11.

Ming, Dongping, et al.. (2018). Segmentation of High Spatial Resolution Remotely Sensed Data Using Watershed with Self-adaptive Parameterization. Yaogan jishu yu yingyong. 33(2). 321–330. 1 indexed citations

12.

Ming, Dongping, et al.. (2016). Applying Spatial Statistics into Remote Sensing Pattern Recognition: with Case Study of Cropland Extraction Based on GeOBIA. SHILAP Revista de lepidopterología. 7 indexed citations

13.

Ming, Dongping. (2009). Research on region partition in high resolution remote sensing image based on GMRF-SVM. Cehui kexue. 1 indexed citations

14.

Luo, Jiancheng, et al.. (2009). Theoretic and Methodological Review on Sensor Information Tupu Computation. Geo-information Science. 11(5). 664–669. 2 indexed citations

15.

Ming, Dongping. (2006). Spatial Data Computing Pattern and Its Geo-Application. Diqiu kexue jinzhan.

16.

Luo, Juhua, et al.. (2006). Multi‐scale information extraction from high resolution remote sensing imagery and region partition methods based on GMRF–SVM. International Journal of Remote Sensing. 28(15). 3395–3412. 13 indexed citations

17.

Shen, Zhanfeng, et al.. (2006). Distributed computing model for processing remotely sensed images based on grid computing. Information Sciences. 177(2). 504–518. 26 indexed citations

18.

Wang, Min, Jiancheng Luo, & Dongping Ming. (2005). Extract ship targets from high spatial resolution remote sensed imagery with shape feature. Editorial Board of Geomatics and Information Science of Wuhan University. 30(8). 17 indexed citations

19.

Shen, Zhanfeng, et al.. (2005). Application of grid computing on remotely sensed image processing. Jisuanji gongcheng. 31(7). 1 indexed citations

20.

Wang, Jing, et al.. (2005). Improved Connected Component Labeling on High-resolution Remote Sensing Image. Computer Engineering and Applications Journal. 41(10). 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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