Feeding a crop-type model its own interannual-fixed historical predictions, encoded as confidence-scaled categorical tokens, raises crop-only F1 by 1.6 points and rebalances precision and recall.
In: NeurIPS (2022)
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Multi-Year Geospatial Reasoning using Interannually-Consistent Historical Predictions as a Free Input Modality
Feeding a crop-type model its own interannual-fixed historical predictions, encoded as confidence-scaled categorical tokens, raises crop-only F1 by 1.6 points and rebalances precision and recall.