Reuse, Don't Retrain: Efficient, Modality-Versatile Geospatial Foundation Models through Feature Distillation

arXiv:2610.32566v1 Announce Type: new Abstract: Geospatial foundation models (GeoFMs) are costly to pretrain, yet model families often repeat this process across backbone scales and sensing modalities. We investigate feature distillation as a more efficient way to reuse existing representations. First, we distill frozen CROMA and TerraMind teachers into smaller Vision Transformers through patch-level feature matching. Across nine PANGAEA tasks, distilled TerraMind Tiny, Small, and Base outperform their scale-matched released counterparts by 2.76, 3.02, and 1.87 mean IoU points, respectively; each also matches or exceeds the next-larger released model. Distilled CROMA-Base gains 2.05 points. TerraMind-Base required only 43 A6000 GPU-hours, approximately 0.5\% of the reported cost of independent pretraining. Second, we introduce TEDI, an unpaired multi-expert approach combining TerraMind for multispectral imagery and DINOv3 for high-resolution RGB. Separate patch embeddings route unpaired inputs into a shared Transformer trunk, removing the need for co-registered data. TEDI-Large achieves the highest single-model mean in our PANGAEA-9 comparison (62.51 mIoU), outperforming both teachers overall while retaining their modality-specific strengths. TEDI-Base reaches 61.75 mIoU with 72\% fewer parameters. These results show that distillation improves the GeoFM accuracy-compute frontier while consolidating complementary experts into a single versatile backbone.