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

Remote sensing multimodal large language models (RS-MLLMs) have improved general aerial-image understanding.

However, Earth observation applications require fine-grained scenario specialization, constrained by scarce high-quality scenario data and incomplete capability coverage.

We formulate this adaptation as a capability-gap-driven post-training problem and propose filling before advancing (FBA).

Rather than relying on single-stage supervised fine-tuning (SFT) over target-domain samples, FBA first fills prerequisite capability gaps before advancing toward scenario specialization.

We instantiate FBA for coastal harbor understanding, a representative multi-source scenario, by constructing CPRS (Coastal-Port Remote Sensing), a three-layer supervision dataset coupled with three ordered stages: (1) RS semantic anchoring for overhead-view visual-language alignment; (2) domain-bridge convergence for shared RS priors across target and bridging scenarios under different modalities; and (3) evidence-grounded scenario tuning for downstream performance.

We construct HarborEval, an eight-track diagnostic benchmark covering perception, spatial understanding, robustness, and generation.

Under comparable training budgets, HarborEval increases from 57.95 with Direct-SFT to 70.29 with FBA on LLaVA-v1.5, and from 81.09 to 83.37 on Qwen3-VL.

FBA also outperforms Collapsed-SFT and leads on harbor-related VRSBench/RSVQA subsets and OpenEval.

Stage-wise and role-replacement analyses validate progressive gap filling and stage-specific roles.

Public examples and release updates for CPRS, HarborEval, code, and trained weights are available at this https URL.