Filling Before Advancing: Capability-Gap-Driven Post-Training for Scenario-Specialized Remote Sensing MLLMs

arXiv:2607.22205v3 Announce Type: replace-cross Abstract: Remote sensing multimodal large language models (RS-MLLMs) have advanced aerial-image understanding, but scenario specialization remains constrained by scarce high-quality supervision and incomplete capability coverage. We propose filling before advancing (FBA), a capability-gap-driven post-training route that develops prerequisite capabilities before scenario-specific adaptation. For coastal harbor understanding, we construct CPRS (Coastal-Port Remote Sensing), a three-layer dataset covering RGB, SAR, PAN, and NIR imagery. FBA organize

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