J. Shao

1.7k total citations · 1 hit paper
34 papers, 1.3k citations indexed

About

J. Shao is a scholar working on Atomic and Molecular Physics, and Optics, Electrical and Electronic Engineering and Spectroscopy. According to data from OpenAlex, J. Shao has authored 34 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Atomic and Molecular Physics, and Optics, 12 papers in Electrical and Electronic Engineering and 10 papers in Spectroscopy. Recurrent topics in J. Shao's work include Semiconductor Quantum Structures and Devices (17 papers), Spectroscopy and Laser Applications (10 papers) and GaN-based semiconductor devices and materials (8 papers). J. Shao is often cited by papers focused on Semiconductor Quantum Structures and Devices (17 papers), Spectroscopy and Laser Applications (10 papers) and GaN-based semiconductor devices and materials (8 papers). J. Shao collaborates with scholars based in United States, China and United Kingdom. J. Shao's co-authors include Kaiyu Li, Sheng Fang, Zhe Li, Michael J. Manfra, Oana Malis, Geoffrey C. Gardner, Sanjay Krishna, Liang Tang, Lina Tang and Quanyi Qiu and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Physics Letters and Journal of Applied Physics.

In The Last Decade

J. Shao

33 papers receiving 1.2k citations

Hit Papers

SNUNet-CD: A Densely Connected Siamese Network for Change... 2021 2026 2022 2024 2021 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
J. Shao United States 16 647 458 296 260 251 34 1.3k
Hock Lim Singapore 17 69 0.1× 243 0.5× 66 0.2× 541 2.1× 159 0.6× 36 1.1k
B. Hancock United States 18 60 0.1× 122 0.3× 60 0.2× 125 0.5× 373 1.5× 39 787
Xiao Xiong China 24 70 0.1× 388 0.8× 74 0.3× 765 2.9× 677 2.7× 90 1.8k
Xiao Wu China 13 290 0.4× 211 0.5× 26 0.1× 18 0.1× 37 0.1× 42 676
P. Martinez France 11 715 1.1× 463 1.0× 187 0.6× 244 0.9× 73 0.3× 43 1.1k
Michael T. Eismann United States 17 985 1.5× 428 0.9× 246 0.8× 138 0.5× 136 0.5× 67 1.4k
Leonid Muratov United States 9 37 0.1× 153 0.3× 82 0.3× 174 0.7× 65 0.3× 21 664
J. Anthony Gualtieri United States 10 459 0.7× 322 0.7× 119 0.4× 21 0.1× 6 0.0× 26 816
Y. Shimizu Japan 11 34 0.1× 52 0.1× 73 0.2× 765 2.9× 151 0.6× 25 1.0k
Jing‐Bo Chen China 18 48 0.1× 145 0.3× 109 0.4× 36 0.1× 286 1.1× 93 1.2k

Countries citing papers authored by J. Shao

Since Specialization
Citations

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

Fields of papers citing papers by J. Shao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of J. Shao

This figure shows the co-authorship network connecting the top 25 collaborators of J. Shao. A scholar is included among the top collaborators of J. Shao 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 J. Shao. J. Shao 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.
Darling, Lindsay, et al.. (2025). Understanding the effects of spatial scaling on the relationship between urban structure and biodiversity. International Journal of Applied Earth Observation and Geoinformation. 138. 104441–104441.
2.
Beneš, Bedřich, et al.. (2025). TreeStructor: Forest Reconstruction With Neural Ranking. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–19. 1 indexed citations
3.
Beneš, Bedřich, et al.. (2025). Errata to “TreeStructor: Forest Reconstruction With Neural Ranking”. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–1. 1 indexed citations
4.
Shao, J., et al.. (2024). LiDAR-Forest Dataset: LiDAR Point Cloud Simulation Dataset for Forestry Application. 112–116. 3 indexed citations
5.
Shao, J., et al.. (2024). Large-scale inventory in natural forests with mobile LiDAR point clouds. SHILAP Revista de lepidopterología. 10. 100168–100168. 5 indexed citations
6.
Tang, Lina, J. Shao, Shiyan Pang, et al.. (2024). Bolstering Performance Evaluation of Image Segmentation Models With Efficacy Metrics in the Absence of a Gold Standard. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–12. 3 indexed citations
7.
Shao, J., Ayman Habib, & Songlin Fei. (2023). SEMANTIC SEGMENTATION OF UAV LIDAR DATA FOR TREE PLANTATIONS. SHILAP Revista de lepidopterología. XLVIII-1/W2-2023. 1901–1906. 1 indexed citations
8.
Lin, Yi-Chun, et al.. (2022). Comparative Analysis of Multi-Platform, Multi-Resolution, Multi-Temporal LiDAR Data for Forest Inventory. Remote Sensing. 14(3). 649–649. 26 indexed citations
9.
Fang, Sheng, Kaiyu Li, J. Shao, & Zhe Li. (2021). SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images. IEEE Geoscience and Remote Sensing Letters. 19. 1–5. 748 indexed citations breakdown →
11.
Shao, J., Lina Tang, Ming Liu, et al.. (2020). BDD-Net: A General Protocol for Mapping Buildings Damaged by a Wide Range of Disasters Based on Satellite Imagery. Remote Sensing. 12(10). 1670–1670. 26 indexed citations
12.
Sun, Lang, et al.. (2019). A Machine Learning-Based Classification System for Urban Built-Up Areas Using Multiple Classifiers and Data Sources. Remote Sensing. 12(1). 91–91. 27 indexed citations
13.
Malis, Oana, et al.. (2014). Quantum band engineering of nitride semiconductors for infrared lasers. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9002. 90021D–90021D. 2 indexed citations
14.
Tang, Liang, M. Cervantes, J. Shao, et al.. (2013). Comparative study of intersubband absorption in AlGaN/GaN and AlInN/GaN superlattices: Impact of material inhomogeneities. Physical Review B. 88(23). 27 indexed citations
16.
Gu, Jiangjiang, Xinwei Wang, Heng Wu, et al.. (2012). 20–80nm Channel length InGaAs gate-all-around nanowire MOSFETs with EOT=1.2nm and lowest SS=63mV/dec. 27.6.1–27.6.4. 28 indexed citations
17.
Sharma, Yagya D., M. N. Kutty, R. V. Shenoi, et al.. (2010). Investigation of multistack InAs/InGaAs/GaAs self-assembled quantum dots-in-double-well structures for infrared detectors. Journal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena. 28(3). C3G1–C3G7. 19 indexed citations
18.
Kutty, M. N., Yashika Sharma, Ajit V. Barve, et al.. (2009). Investigation of multi-stack quantum dots-in-double-well infrared detectors. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7467. 74670V–74670V. 2 indexed citations
19.
Shenoi, R. V., J. Shao, Yamini Sharma, et al.. (2008). Low-strain InAs∕InGaAs∕GaAs quantum dots-in-a-well infrared photodetector. Journal of Vacuum Science & Technology B Microelectronics and Nanometer Structures Processing Measurement and Phenomena. 26(3). 1136–1139. 46 indexed citations
20.
Shao, J., et al.. (2007). Resonant cavity enhanced InAs∕In0.15Ga0.85As dots-in-a-well quantum dot infrared photodetector. Journal of Vacuum Science & Technology B Microelectronics and Nanometer Structures Processing Measurement and Phenomena. 25(4). 1186–1190. 31 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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