AI RESEARCH
LEPA: Learning Geometric Equivariance in Satellite Remote Sensing Data with a Predictive Architecture
arXiv CS.CV
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ArXi:2603.07246v1 Announce Type: new Geospatial foundation models provide precomputed embeddings that serve as compact feature vectors for large-scale satellite remote sensing data. While these embeddings can reduce data-transfer bottlenecks and computational costs, Earth observation (EO) applications can still face geometric mismatches between user-defined areas of interest and the fixed precomputed embedding grid.