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About this paper
Yuchi Ma, Jiaqi Yang, Mengyu Liang, Ivan Higuera-Mendieta, Caleb Robinson, Anthony Ortiz, Min Chen, David B. Lobell and 2 more
The authors evaluate Earth embeddings against conventional features across remote-sensing tasks, including tree-crown segmentation and soybean-yield prediction. Using matched baselines, they find that high-resolution NAIP imagery outperforms embedding-only and imagery-plus-embedding models for crown segmentation, while combining AlphaEarth Foundations embeddings with remote-sensing and weather features gives the best soybean-yield results. The findings support task-dependent complementarity, not a general advantage.
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