Kai Cui

545 total citations · 1 hit paper
28 papers, 345 citations indexed

About

Kai Cui is a scholar working on Ecology, Aerospace Engineering and Atmospheric Science. According to data from OpenAlex, Kai Cui has authored 28 papers receiving a total of 345 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Ecology, 12 papers in Aerospace Engineering and 10 papers in Atmospheric Science. Recurrent topics in Kai Cui's work include Avian ecology and behavior (7 papers), Remote Sensing in Agriculture (6 papers) and Remote Sensing and LiDAR Applications (4 papers). Kai Cui is often cited by papers focused on Avian ecology and behavior (7 papers), Remote Sensing in Agriculture (6 papers) and Remote Sensing and LiDAR Applications (4 papers). Kai Cui collaborates with scholars based in China, Australia and United Kingdom. Kai Cui's co-authors include Jing Xiang, Cheng Hu, Rui Wang, Dongli Wu, Huayu Li, Yi Sui, Teng Long, Xiaojian Xu, Jingmin Zhang and Kongming Wu and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, Remote Sensing and Neural Computing and Applications.

In The Last Decade

Kai Cui

24 papers receiving 338 citations

Hit Papers

Extracting Diurnal Activity Patterns of Birds in Communal... 2024 2026 2025 2024 10 20 30 40 50
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Extracting Diurnal Activity Patterns of Birds in Communal Roosts From Polarimetric Weather Radar Data
    2024 54 citations Cheng Hu, Kai Cui et al. IEEE Transactions on Geoscience and Remote Sensing profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kai Cui China 8 84 54 48 45 45 28 345
Chia-Yu Hsu United States 11 43 0.5× 62 1.1× 40 0.8× 100 2.2× 61 1.4× 21 559
Elcio Hideiti Shiguemori Brazil 12 94 1.1× 121 2.2× 46 1.0× 25 0.6× 63 1.4× 66 449
Jiao Guo China 10 71 0.8× 107 2.0× 49 1.0× 21 0.5× 42 0.9× 43 311
Junjun Wu China 13 60 0.7× 22 0.4× 36 0.8× 19 0.4× 31 0.7× 46 415
Xubing Yang China 14 67 0.8× 69 1.3× 32 0.7× 30 0.7× 84 1.9× 45 585
Decheng Wang China 9 68 0.8× 19 0.4× 102 2.1× 81 1.8× 156 3.5× 58 409
Dı́bio Leandro Borges Brazil 14 66 0.8× 49 0.9× 15 0.3× 42 0.9× 63 1.4× 44 569
Shuai Feng China 12 63 0.8× 30 0.6× 24 0.5× 11 0.2× 40 0.9× 41 489

Countries citing papers authored by Kai Cui

Since Specialization
Citations

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

Fields of papers citing papers by Kai Cui

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kai Cui

This figure shows the co-authorship network connecting the top 25 collaborators of Kai Cui. A scholar is included among the top collaborators of Kai Cui 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 Kai Cui. Kai Cui 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
1.
Hu, Cheng, et al.. (2024). Extracting Bird and Insect Migration Echoes From Single-Polarization Weather Radar Data Using Semi-Supervised Learning. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–12. 4 indexed citations
2.
Hu, Cheng, et al.. (2024). An Animal Migration Forecast Model With Weather Radar and Meteorological Data. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–13. 1 indexed citations
3.
Zhou, Liang, et al.. (2024). Multi-spectral radiation thermometry of space point targets based on spectral image pixel binning. Applied Optics. 63(30). 7900–7900.
4.
Hu, Cheng, et al.. (2024). Superpixel-Based Weak Biological Feature Echo Extraction Method for Weather Radar. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–13. 23 indexed citations
5.
Liu, Xuan, et al.. (2024). A dynamic short-range animal migration forecast model based on weather radar network. IET conference proceedings.. 2023(47). 4045–4049. 2 indexed citations
6.
Li, Jiayi, et al.. (2024). A quantitative estimation method of migrating insects using weather radar by high resolution radar joint observation. IET conference proceedings.. 2023(47). 4040–4044.
7.
Hu, Cheng, et al.. (2024). Extracting Diurnal Activity Patterns of Birds in Communal Roosts From Polarimetric Weather Radar Data. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–12. 54 indexed citations breakdown →
8.
Wang, Rui, et al.. (2023). Quantification of Migration Birds Based on Polarimetric Weather Radar. Remote Sensing. 15(19). 4809–4809. 4 indexed citations
9.
Hu, Cheng, Joshua Soderholm, Jason W. Chapman, et al.. (2022). Prospects for monitoring bird migration along the East Asian‐Australasian Flyway using weather radar. Remote Sensing in Ecology and Conservation. 9(2). 169–181. 15 indexed citations
10.
Wang, Rui, et al.. (2022). High-Resolution and Low Blind Range Waveform for Migratory Insects’ Taking-Off and Landing Behavior Observation. Remote Sensing. 14(13). 3034–3034. 4 indexed citations
11.
Wang, Rui, et al.. (2022). Insect-Equivalent Radar Cross-Section Model Based on Field Experimental Results of Body Length and Orientation Extraction. Remote Sensing. 14(3). 508–508. 3 indexed citations
12.
Hu, Cheng, et al.. (2021). Animal Migration Patterns Extraction Based on Atrous-Gated CNN Deep Learning Model. Remote Sensing. 13(24). 4998–4998. 8 indexed citations
13.
Cui, Kai, et al.. (2020). Deep-learning-based extraction of the animal migration patterns from weather radar images. Science China Information Sciences. 63(4). 28 indexed citations
14.
Hu, Cheng, et al.. (2019). A Retrieval Method of Vertical Profiles of Reflectivity for Migratory Animals Using Weather Radar. IEEE Transactions on Geoscience and Remote Sensing. 58(2). 1030–1040. 14 indexed citations
15.
Wang, Yixuan, et al.. (2019). Insect trajectory simulation method based on radar observation. The Journal of Engineering. 2019(21). 7365–7369. 1 indexed citations
16.
Cui, Kai, et al.. (2019). Quantifying insect migration across Bohai strait using weather radar. The Journal of Engineering. 2019(19). 6095–6098. 5 indexed citations
17.
Dong, Xichao, Dandan Zhang, Kai Cui, Cheng Hu, & Xin Lv. (2016). Spatial filtering strategies on deforestation detection using SAR image textures. 1–4. 2 indexed citations
18.
Dong, Xichao, S. Quegan, Wei Liu, Kai Cui, & Xin Lv. (2015). Improving Tropical Deforestation Detection by Fusing Multiple SAR Change Measures. 5 .–5 .. 2 indexed citations
19.
Cui, Kai, et al.. (2011). EM Backscattering of Simplified Ship Model over Sea Surface Based on a High Frequency Hybrid Method. JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY. 30(6). 1500–1503.
20.

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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