GeoCore-9B: Towards Geo-Aware Generative Foundation Models in Earth Observation
Existing generative models for earth observation (EO) predominantly rely on fine-tuning natural image priors, which limits their scalability and introduces perspective biases that conflict with geospatial constraints. To address this, we introduce GeoCore-9B, a 9-billion-parameter generative foundation model, which is the first of its scale to be trained from scratch exclusively on EO data. Unlike previous EO foundation models, GeoCore-9B is built upon a Flow Matching-based Diffusion Transformer (DiT) and natively conditions generation on text descriptions and continuous geospatial metadata, including ground sample distances, latitudes, and longitudes. To overcome the convergence and spat...
| Authors | Jeonghyeok Do, Munchurl Kim |
|---|---|
| Primary category | cs.CV |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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