# Lance

Remote-sensing image fusion and semantic interpretation have usually been optimized separately, so good fusion metrics need not preserve downstream retrieval or detection utility. Lance maps each input to a closed-vocabulary text record of scene, objects and layout, conditions fusion on that language, re-describes the fused image as text, and back-propagates through text consistency and an anti-collapse text–mask contrastive loss with negative queries. Across seven test sets on GF2, QuickBird, WorldView-III and WorldView-II, reduced-scale reference metrics improve by up to 11.7% (median 2.1%) over the strongest learning baseline, and a held-out RemoteCLIP verifier plus a supervised DOTA detector give +6.4 mAP@0.5 over the closest competitor.

Remote-sensing image fusion and semantic interpretation have usually been optimized separately, so good fusion metrics need not preserve downstream retrieval or detection utility. Lance maps each input to a closed-vocabulary text record of scene, objects and layout, conditions fusion on that language, re-describes the fused image as text, and back-propagates through text consistency and an anti-collapse text–mask contrastive loss with negative queries. Across seven test sets on GF2, QuickBird, WorldView-III and WorldView-II, reduced-scale reference metrics improve by up to 11.7% (median 2.1%) over the strongest learning baseline, and a held-out RemoteCLIP verifier plus a supervised DOTA detector give +6.4 mAP@0.5 over the closest competitor.