Chunxiang Wang
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- Advanced Neural Network Applications 32
- Video Surveillance and Tracking Methods 26
- Robotic Path Planning Algorithms 18
- Automotive Engineering top 2%
- Autonomous Vehicle Technology and Safety 44
- Geology top 2%
- 3D Surveying and Cultural Heritage 15
- Aerospace Engineering top 2%
- Robotics and Sensor-Based Localization 42
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- Remote Sensing and LiDAR Applications 24
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- Advancements in Battery Materials 13
- Co-authors
- Ming YangYeqiang QianLiuyuan DengLiang LiBing WangZhifeng LiYujing JiangBing Hu
In The Last Decade
Chunxiang Wang
159 papers receiving 2.0k citations
Peers
Comparison fields: 5 of 125
- Computer Vision and Pattern Recognition 864
- Automotive Engineering 477
- Geology 173
- Aerospace Engineering 520
- Safety, Risk, Reliability and Quality 154
Countries citing papers authored by Chunxiang Wang
This map shows the geographic impact of Chunxiang Wang'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 Chunxiang Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chunxiang Wang more than expected).
Fields of papers citing papers by Chunxiang Wang
This network shows the impact of papers produced by Chunxiang Wang. 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 Chunxiang Wang. The network helps show where Chunxiang Wang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Chunxiang Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 2 | |
| 3 | 2024 | 11 | |
| 4 | 2024 | 2 | |
| 5 | 2024 | 1 | |
| 6 | 2024 | 2 | |
| 7 | 2024 | 0 | |
| 8 | 2024 | 25 | |
| 9 | 2023 | 0 | |
| 10 | 2023 | 4 | |
| 11 | 2023 | 1 | |
| 12 | 2022 | 3 | |
| 13 | 2020 | 11 | |
| 14 | 2020 | 5 | |
| 15 | 2020 | 8 | |
| 16 | 2019 | 27 | |
| 17 | High resolution remote sensing image segmentation based on multi-features fusion | 2017 | 4 |
| 18 | August 2014 Hiroshima landslide disaster and its societal impact | 2015 | 1 |
| 19 | Localization System for Intelligent Vehicles Based on Magneto-resistive Sensor | 2008 | 0 |
| 20 | The Tension Control System Based on Neural Network | 2001 | 2 |
About Chunxiang Wang
Chunxiang Wang is a scholar working on Automotive Engineering, Computer Vision and Pattern Recognition and Geology, having authored 173 papers that have together received 2.0k indexed citations. Recurring topics across this work include Autonomous Vehicle Technology and Safety (44 papers), Robotics and Sensor-Based Localization (42 papers), Advanced Neural Network Applications (32 papers), Video Surveillance and Tracking Methods (26 papers), Remote Sensing and LiDAR Applications (24 papers), Robotic Path Planning Algorithms (18 papers), 3D Surveying and Cultural Heritage (15 papers) and Advancements in Battery Materials (13 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (864 citations), Automotive Engineering (477 citations) and Geology (173 citations). Chunxiang Wang has collaborated with scholars based in China, France and Japan. Frequent co-authors include Ming Yang, Yeqiang Qian, Liuyuan Deng, Liang Li, Bing Wang, Bing Wang, Bing Wang, Zhifeng Li, Bing Wang and Yujing Jiang.
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.