Automated Quality Flagging
Automated Quality Flagging is a computational methodology that systematically identifies and annotates anomalies or inaccuracies in elevation or yield data through algorithmic rules or statistical thresholds, distinguishing it from manual inspection by enabling scalable, consistent, and real-time data integrity assessment.
16
Publications
1.9K
Citations
134
Authors
81
Institutions
Publications per year
2017–2026
2
16
An Open Resource for Non-human Primate Imaging
Michael P. Milham, Lei Ai, Bonhwang Koo et al. · Neuron · 2018 · 282 citations · Full text
Mohamed S. Rashed, Martin P. Bucknall, Douglas Little et al. · Clinical Chemistry · 1997 · 274 citations · Full text
Life Sciences, Biochemistry, Mass Spectrometric Analysis +16
APEX - the Hyperspectral ESA Airborne Prism Experiment
K.I. Itten, Francesco Dell’Endice, Andreas Hueni et al. · Sensors · 2008 · 121 citations · Full text
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81

Cornell University
Ithaca, United States
3 h-index
7 publications
153 citations
Oregon State University
Corvallis, United States
2 h-index
4 publications
96 citations

Centre National de la Recherche Scientifique
Paris, France
2 h-index
8 publications
2.3K citations
Resonance Research (United States)
Billerica, United States
1 h-index
2 publications
564 citations

Kennedy Krieger Institute
Baltimore, United States
1 h-index
1 publications
282 citations
McGovern Institute for Brain Research
Cambridge, United States
1 h-index
1 publications
282 citations
New York University
New York, United States
1 h-index
1 publications
282 citations
Massachusetts Institute of Technology
Cambridge, United States
1 h-index
1 publications
282 citations
1–8 of 81
1 publications · 21 citations
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VZJ · 1 publications · 633 citations
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JAMC · 1 publications · 113 citations
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1 publications · 19 citations
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TOASCJ · 1 publications · 249 citations
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