How do Self-Supervised Remote Sensing Vision Models Transfer to Downstream Tasks?
Self-supervised geospatial foundation models (GeoFMs) learn transferable representations from remote sensing data, but their downstream behavior is difficult to characterize. We study six representative GeoFMs spanning joint-embedding, reconstruction, and multimodal pretraining families, and evaluate transfer across classification, regression, and segmentation benchmarks under different label availability and downstream pipelines. We find that model rankings change across tasks and adaptation settings. Layerwise probing shows that, in most cases, task-relevant information is more accessible in intermediate transformer blocks compared to final-layer embeddings, and that GeoFMs exhibit dist...
| Authors | Julia Romero, Qin Lv, Morteza Karimzadeh |
|---|---|
| Primary category | cs.CV |
| Published | 11 June 2026 |
| Last updated at source | 11 June 2026 |
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