Geospatial Foundational Embedder: Top-1 Winning Solution on EarthVision Embed2Scale Challenge (CVPR 2025)
This work addresses the problem of creating foundational models for geospatial data to facilitate downstream tasks like classification and regression, but it appears incremental as it builds on existing challenges and methods.
The authors tackled the EarthVision Embed2Scale challenge by developing a foundational geospatial model to embed hyperspectral data cubes, achieving a Top-1 winning solution.
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.