Ming Cheng

2.3k total citations
73 papers, 1.6k citations indexed

About

Ming Cheng is a scholar working on Computer Vision and Pattern Recognition, Environmental Engineering and Geology. According to data from OpenAlex, Ming Cheng has authored 73 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Computer Vision and Pattern Recognition, 25 papers in Environmental Engineering and 23 papers in Geology. Recurrent topics in Ming Cheng's work include Remote Sensing and LiDAR Applications (25 papers), 3D Surveying and Cultural Heritage (23 papers) and Robotics and Sensor-Based Localization (14 papers). Ming Cheng is often cited by papers focused on Remote Sensing and LiDAR Applications (25 papers), 3D Surveying and Cultural Heritage (23 papers) and Robotics and Sensor-Based Localization (14 papers). Ming Cheng collaborates with scholars based in China, Canada and United States. Ming Cheng's co-authors include Cheng Wang, Jonathan Li, Chenglu Wen, Xin Li, Huan Luo, Yiping Chen, Qing Li, Shaoyang Chen, Dawei Zai and Yulan Guo and has published in prestigious journals such as SHILAP Revista de lepidopterología, International Journal of Molecular Sciences and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Ming Cheng

67 papers receiving 1.5k citations

Peers

Ming Cheng
Comparison fields: 5 of 107
  • Environmental Engineering 644
  • Computer Vision and Pattern Recognition 575
  • Geology 498
  • Aerospace Engineering 416
  • Computational Mechanics 236
Replace Suya You with:
Suya You United States
Chenglu Wen China
Huan Luo China
Shuhan Shen China
Nicolas Vandapel United States
Hanyun Wang China
Stefano Rosa Italy
Christoph Mertz United States
Bruno Vallet France
Suya You United States View profile →
Citations per field, relative to Ming Cheng
Ming Cheng · 1×
Citations per year, relative to Ming Cheng
Ming Cheng · 1×

Countries citing papers authored by Ming Cheng

Since Specialization
Citations

This map shows the geographic impact of Ming Cheng'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 Ming Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming Cheng more than expected).

Fields of papers citing papers by Ming Cheng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ming Cheng. 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 Ming Cheng. The network helps show where Ming Cheng may publish in the future.

Co-authorship network of co-authors of Ming Cheng

This figure shows the co-authorship network connecting the top 25 collaborators of Ming Cheng. A scholar is included among the top collaborators of Ming Cheng 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 Ming Cheng. Ming Cheng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 0
2 1
3 10
4 4
5 4
6 6
7 7
8 14
9 2
10 9
11 18
12 7
13 11
14 58
15 10
16 40
17 52
18 3
19 102
20 0

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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