Moses Tangwam: h-index, Total Citations, and Citation Map
Moses Tangwam's h-index is 4 (4 i10-index, 55+ total citations across 6+ publications) according to Google Scholar as of May 2026. Moses Tangwam is affiliated with MGIS, Michigan Technological University.
Moses Tangwam is a researcher affiliated with MGIS, Michigan Technological University, specializing in GNSS, GIS, Remote Sensing. Their work has been cited 55 times. This profile visualizes their global influence, highlighting strong citation networks in Canada.
Moses Tangwam's Citation Metrics
Bibliometric impact based on 6 indexed publications. Of these, 5 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 4
- i10-Index
- 4
- Total Citations
- 55
- Citing Countries
- 6
As of May 2026.
Moses Tangwam has an h-index of 4 and 55 total citations across 6 publications, with research cited by institutions in 6 countries.
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Top Cited Works
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Determination of Gravity Potential Differences and Height Systems for Survey Control on KNUST Campus, Ghana
202516
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Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
The researcher advanced geospatial analysis in southern Ghana by developing methods to process 3D positions from CORS data, establishing a foundational reference for regional surveying.
The researcher developed a geodetic framework for monitoring structural deformation in steel bridges, demonstrated through the Pungu-Gumongo case study.
The researcher advanced robust buckthorn classification methods specifically designed to overcome performance degradation in shadowed environments, addressing a critical gap in computer vision for ecological monitoring.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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