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Many machine learning (ML) applications rely on expert labels, and qualified experts may provide different but plausible interpretations of the same observation. Such variation across expert labels may reflect genuine disagreement or ambiguity rather than annotation error. When individual experts additionally report in...
This paper proposes a two-module framework where first Multiple Instance Learning is used for water--ice classification, and then a multi-label proportion learning (MLPL) is introduced for ice-type composition prediction, and extends this framework with a multimodal model that integrates SAR imagery with AMSR2 brightne...
Arctic maritime navigation is becoming increasingly important as changing sea-ice conditions expand seasonal accessibility while simultaneously introducing substantial operational, environmental, and community risks. Arctic route planning is inherently a multi-criteria problem: routes that improve vessel safety or effi...
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