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Geospatial Foundational Embedder: Top-1 Winning Solution on EarthVision Embed2Scale Challenge (CVPR 2025)

Computer Vision and Pattern Recognition 2025-09-10 v1
Authors: Zirui Xu , Raphael Tang , Mike Bianco , Qi Zhang , Rishi Madhok , Nikolaos Karianakis , Fuxun Yu
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Abstract

EarthVision Embed2Scale challenge (CVPR 2025) aims to develop foundational geospatial models to embed SSL4EO-S12 hyperspectral geospatial data cubes into embedding vectors that faciliatetes various downstream tasks, e.g., classification, regression, etc. In this technical report, we introduce our proposed method for the Top-1 winning solution on the Embed2Scale Challenge.

Cite

@article{arxiv.2509.06993,
  title  = {Geospatial Foundational Embedder: Top-1 Winning Solution on EarthVision Embed2Scale Challenge (CVPR 2025)},
  author = {Zirui Xu and Raphael Tang and Mike Bianco and Qi Zhang and Rishi Madhok and Nikolaos Karianakis and Fuxun Yu},
  journal= {arXiv preprint arXiv:2509.06993},
  year   = {2025}
}

Comments

CVPR 2025 EarthVision Embed2Scale challenge Top-1 Winning Solution

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