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Lat-lon-flat machine learning — feature extraction and detection that operate on the equirectangular projection of the same fields, exploiting only translation equivariance in pixel space.
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Sphere-aware machine learning — feature extraction and detection that operate directly on a HEALPix-NESTED substrate, exploiting rotation equivariance on the sphere.
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Detection accuracy and F1 score by test latitude band, given a model trained on low-latitude events only.
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Globally-distributed sea-surface-temperature fields on the sphere with marine-heatwave events occurring at any latitude.
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For globally-distributed marine-heatwave detection on sea-surface-temperature data (Population), does a sphere-aware machine-learning approach that operates on a HEALPix substrate (Intervention) recover the high-latitude detection accuracy lost by an equivalent lat-lon-flat machine-learning approach (Comparator) when both are trained on low-latitude events and tested at progressively higher latitudes, measured by detection accuracy and F1 score per test latitude band (Outcome)?
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Does sphere-aware ML on HEALPix recover detection accuracy at high latitudes that lat-lon flat ML loses?
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Anne Fouilloux
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