ScalSpectra: A Scalable Any-Spectral Foundation Model for Earth Observation

We propose ScalSpectra, a self-supervised framework for multimodal Earth observation (EO) foundation model pretraining. Although several pioneering efforts have emerged in this field, existing approaches still suffer from two major limitations. First, different EO sensors provide different sets of spectral bands, and the contribution of each band to the fused representation depends on which other bands are observed alongside it. Current tokenizers do not explicitly adapt band fusion to the observed band set, leaving this cross-band dependency unmodeled in the fused representation. Second, existing multimodal EO foundation models predominantly rely on masked image modeling and contrastive self-distillation objectives that require large-scale, high-quality data to learn dense semantic features, whereas spectral observations are relatively scarce, costly to acquire, and complex to process. Our framework is built on spectral-aware dynamic band tokenization (SADBT) and dual-teacher dense feature learning. SADBT encodes band content together with optical wavelengths or SAR polarization and orbit descriptors, and uses a configuration-aware query to fuse the available bands into physically informed patch embeddings, so a single model scales to any band combination. The dual-teacher framework combines multimodal local structural representation learning (MLSRL), which aligns masked student features with an unmasked exponential moving average teacher over all patch positions, and cross-modal semantic representation distillation (CMSRD), which transfers dense semantic knowledge from a frozen visual-prior teacher through aligned optical observations while retaining modality-specific evidence. We pretrain ScalSpectra on heterogeneous EO data sources spanning RGB, multispectral, hyperspectral, and SAR observations. Extensive evaluations across scene classification, semantic segmentation, change detection, and domain- generalized dense prediction demonstrate that ScalSpectra achieves the best overall performance across the evaluated benchmarks.