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arXiv Computer Vision By Priyanka Goyal, Maitreyee Sar, Adway Mitra, Manjira Sinha

Evaluating AlphaEarth Embeddings for Crop Classification: Separability, Temporal Dynamics, and Cross-Regional Transferability

Read the original on arXiv Computer Vision →

The paper introduces a framework to evaluate AlphaEarth embeddings for crop classification across spatial, temporal, and cross-regional dimensions. It compares a 64‑dimensional embedding space with traditional Sentinel‑2, NDVI, and harmonic features using field data from the US and India, finding that embeddings offer superior distribution‑aware separability and capture multi‑year crop rotation dynamics, while traditional features transfer better across continents. The study highlights a trade‑off between representational richness and cross‑region transferability, suggesting a hybrid approach for large‑scale crop classification.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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