{"ID":2874865,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.06993","arxiv_id":"2509.06993","title":"Geospatial Foundational Embedder: Top-1 Winning Solution on EarthVision Embed2Scale Challenge (CVPR 2025)","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.","short_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 th...","url_abs":"https://arxiv.org/abs/2509.06993","url_pdf":"https://arxiv.org/pdf/2509.06993v1","authors":"[\"Zirui Xu\",\"Raphael Tang\",\"Mike Bianco\",\"Qi Zhang\",\"Rishi Madhok\",\"Nikolaos Karianakis\",\"Fuxun Yu\"]","published":"2025-09-03T04:10:23Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}