Inkyu Sa
- Analytical Chemistry top 1%
- Plant Science top 2%
- Smart Agriculture and AI 11
-
- Robotic Path Planning Algorithms 8
- Advanced Vision and Imaging 8
- Advanced Image and Video Retrieval Techniques 4
- Aerospace Engineering top 2%
- Robotics and Sensor-Based Localization 22
- Ecology top 5%
- Remote Sensing in Agriculture 3
-
- Robotics and Automated Systems 6
-
- Modular Robots and Swarm Intelligence 4
Inkyu Sa
39 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 115
- Analytical Chemistry 386
- Plant Science 1.4k
- Computer Vision and Pattern Recognition 647
- Aerospace Engineering 500
- Ecology 448
Countries citing papers authored by Inkyu Sa
This map shows the geographic impact of Inkyu Sa'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 Inkyu Sa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Inkyu Sa more than expected).
Fields of papers citing papers by Inkyu Sa
This network shows the impact of papers produced by Inkyu Sa. 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 Inkyu Sa. The network helps show where Inkyu Sa may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Inkyu Sa, 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 | 2024 | 4 | |
| 2 | 2024 | 8 | |
| 3 | 2022 | 18 | |
| 4 | 2022 | 50 | |
| 5 | 2020 | 76 | |
| 6 | 2018 | 40 | |
| 7 | 2018 | 16 | |
| 8 | 2018 | 64 | |
| 9 | 2017 | 43 | |
| 10 | 2017 | 93 | |
| 11 | 2016 | 70 | |
| 12 | 2016 | 61 | |
| 13 | On Visual Detection of Highly-occluded Objects for Harvesting Automation in Horticulture | 2015 | 7 |
| 14 | A Bayesian framework for the assessment of vision-based weed and fruit detection and classification algorithms | 2015 | 2 |
| 15 | 2015 | 16 | |
| 16 | Inspection of pole-like structures using a vision-controlled VTOL UAV and shared autonomy | 2014 | 3 |
| 17 | Improved line tracker using IMU and Vision for visual servoing | 2013 | 2 |
| 18 | 100Hz onboard vision for quadrotor state estimation | 2012 | 7 |
| 19 | Estimation and control for an open-source quadcopter | 2011 | 1 |
| 20 | 2008 | 18 |
About Inkyu Sa
Inkyu Sa is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering, Geology, Control and Systems Engineering and Human-Computer Interaction, having authored 41 papers that have together received 2.3k indexed citations. Recurring topics across this work include Robotics and Sensor-Based Localization (22 papers), Smart Agriculture and AI (11 papers), Robotic Path Planning Algorithms (8 papers), Advanced Vision and Imaging (8 papers), Robotics and Automated Systems (6 papers), Modular Robots and Swarm Intelligence (4 papers), Advanced Image and Video Retrieval Techniques (4 papers) and Remote Sensing in Agriculture (3 papers). The work is most often cited by research in Analytical Chemistry (386 citations), Plant Science (1.4k citations), Computer Vision and Pattern Recognition (647 citations), Aerospace Engineering (500 citations) and Ecology (448 citations). Inkyu Sa has collaborated with scholars based in Australia, Switzerland and South Korea. Frequent co-authors include Chris McCool, Tristán Pérez, Ben Upcroft, Feras Dayoub, Zongyuan Ge, Roland Siegwart, Juan Nieto, Zetao Chen, Marija Popović and Chris Lehnert. Their work appears in journals such as IEEE Robotics and Automation Letters, Journal of Field Robotics, Sensors, IEEE Transactions on Consumer Electronics and Remote Sensing.
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.