Wen Yao
- Automotive Engineering top 5%
- Computer Vision and Pattern Recognition top 5%
- Control and Systems Engineering top 10%
- Environmental Engineering top 10%
- Artificial Intelligence
- Co-authors
- Huijing ZhaoHongbin ZhaXijun ZhaoDonghao XuPhilippe BonnifaitBiao GaoFranck DavoineLang Xia
- Topics
- Autonomous Vehicle Technology and Safety (10 papers)Traffic control and management (6 papers)Remote Sensing and LiDAR Applications (5 papers)
- Journals
- IEEE Transactions on Intelligent Transportation SystemsRemote SensingComputers and Electronics in Agriculture
- Partner nations
- ChinaUnited StatesFrance
In The Last Decade
Wen Yao
26 papers receiving 467 citations
Peers
Comparison fields: 5 of 83
- Automotive Engineering 193
- Computer Vision and Pattern Recognition 152
- Control and Systems Engineering 99
- Environmental Engineering 95
- Artificial Intelligence 62
Countries citing papers authored by Wen Yao
This map shows the geographic impact of Wen Yao'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 Wen Yao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wen Yao more than expected).
Fields of papers citing papers by Wen Yao
This network shows the impact of papers produced by Wen Yao. 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 Wen Yao. The network helps show where Wen Yao may publish in the future.
Co-authorship network of co-authors of Wen Yao
This figure shows the co-authorship network connecting the top 25 collaborators of Wen Yao. A scholar is included among the top collaborators of Wen Yao 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 Wen Yao. Wen Yao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 4 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 6 | |
| 7 | 1 | |
| 8 | 58 | |
| 9 | 1 | |
| 10 | 12 | |
| 11 | 26 | |
| 12 | 2 | |
| 13 | 29 | |
| 14 | 12 | |
| 15 | 63 | |
| 16 | 52 | |
| 17 | Ground-based Visible Cloud Image Classification Method Based on KNN Algorithm | 2 |
| 18 | 39 | |
| 19 | 16 | |
| 20 | 5 |
About Wen Yao
Wen Yao is a scholar working on Automotive Engineering, Small Animals and Computer Vision and Pattern Recognition, having authored 27 papers that have together received 484 indexed citations. Recurring topics across this work include Autonomous Vehicle Technology and Safety (10 papers), Traffic control and management (6 papers) and Remote Sensing and LiDAR Applications (5 papers). The work is most often cited by research in Automotive Engineering (193 citations), Computer Vision and Pattern Recognition (152 citations) and Environmental Engineering (95 citations). Wen Yao has collaborated with scholars based in China, United States and France. Frequent co-authors include Huijing Zhao, Hongbin Zha, Xijun Zhao, Donghao Xu, Philippe Bonnifait, Biao Gao, Franck Davoine, Lang Xia, Liping Chen and Ruirui Zhang. Their work appears in journals such as IEEE Transactions on Intelligent Transportation Systems, Remote Sensing and Computers and Electronics in Agriculture.
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.