Geospatial Foundational Embedder: Top-1 Winning Solution on EarthVision Embed2Scale Challenge (CVPR 2025)
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Abstract (arXiv)
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.
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References · 6
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
- 2023RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensingcited by 151
- 2021Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalizationcited by 70
- 2024TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learningcited by 31
- 2025SSL4EO-S12 v1.1: A Multimodal, Multiseasonal Dataset for Pretraining, Updatedcited by 9
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