# SWIMS

Remote sensing change detection usually depends on large collections of temporally aligned, pixel-level annotations, which limits scaling across regions and platforms. SpotDiff composes DINOv3 semantic features, AnyUp pixel-resolution feature reconstruction and SAM 3 text-guided region proposals into a training-free pipeline, then compares the cosine similarity of the two dates' reconstructed features inside the proposed regions. On LEVIR-CD the ViT-7B version reaches 82.46 F1, 2.06 points above the strongest prior training-free method; on SECOND it raises building-change IoU by 10.3.

Remote sensing change detection usually depends on large collections of temporally aligned, pixel-level annotations, which limits scaling across regions and platforms. SpotDiff composes DINOv3 semantic features, AnyUp pixel-resolution feature reconstruction and SAM 3 text-guided region proposals into a training-free pipeline, then compares the cosine similarity of the two dates' reconstructed features inside the proposed regions. On LEVIR-CD the ViT-7B version reaches 82.46 F1, 2.06 points above the strongest prior training-free method; on SECOND it raises building-change IoU by 10.3.