AlphaEarth embeddings evaluated for crop classification across US and India
64-dimensional embeddings separate similar crops better than NDVI or harmonics, but transfer worse from the US to India.
64-dimensional embeddings separate similar crops better than NDVI or harmonics, but transfer worse from the US to India.
On 17 tasks LoCA has the most wins and is the only model ahead on a majority of tasks.
On 17 tasks LoCA wins 68.8% of per-task comparisons and is the only encoder ahead of every rival on a majority of tasks.
Architecture search yields a 409-parameter Sentinel-2 bloom model, lifting AUC to 0.820.
PAtteRNS factorizes temporal, spectral, and spatial attention for Sentinel-2 cropland segmentation.
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