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Prior studies on Remote Sensing Foundation Model (RSFM) reveal immensepotential towards a generic model for Earth Observation. Nevertheless, theseworks primarily focus on a single modality without temporal and geo-contextmodeling, hampering their capabilities for diverse tasks. In this study, wepresent SkySense, a generic billion-scale model, pre-trained on a curatedmulti-modal Remote Sensing Imagery (RSI) dataset with 21.5 million temporalsequences. SkySense incorporates a factorized multi-modal spatiotemporalencoder taking temporal sequences of optical and Synthetic Aperture Radar (SAR)data as input. This encoder is pre-trained by our proposed Multi-GranularityContrastive Learning to learn representations across different modal andspatial granularities. To further enhance the RSI representations by thegeo-context clue, we introduce Geo-Context Prototype Learning to learnregion-aware prototypes upon RSI's multi-modal spatiotemporal features. To ourbest knowledge, SkySense is the largest Multi-Modal RSFM to date, whose modulescan be flexibly combined or used individually to accommodate various tasks. Itdemonstrates remarkable generalization capabilities on a thorough evaluationencompassing 16 datasets over 7 tasks, from single- to multi-modal, static totemporal, and classification to localization. SkySense surpasses 18 recentRSFMs in all test scenarios. Specifically, it outperforms the latest modelssuch as GFM, SatLas and Scale-MAE by a large margin, i.e., 2.76%, 3.67% and3.61% on average respectively. We will release the pre-trained weights tofacilitate future research and Earth Observation applications.

SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery