# SAM 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.

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.

Quantizing SAM 3's image-encoder weights to 4 bits while keeping activations at 8 bits produces prompt-alignment drift: masks look plausible but land on the wrong object or lose the target across video frames. SPQ gives more calibration weight to parameters that affect text-concept scores, constrains feature error along low-variance residual directions, and folds a mean-error correction estimated from paired video rollouts into existing biases. Under W4A8, RefCOCO Pr@0.5 rises from 42.1 for PTQ4SAM to 72.5 while retaining 54.8 COCO mask AP, with no added inference operators.

Quantizing SAM 3's image-encoder weights to 4 bits while keeping activations at 8 bits produces prompt-alignment drift: masks look plausible but land on the wrong object or lose the target across video frames. SPQ gives more calibration weight to parameters that affect text-concept scores, constrains feature error along low-variance residual directions, and folds a mean-error correction estimated from paired video rollouts into existing biases. Under W4A8, RefCOCO Pr@0.5 rises from 42.1 for PTQ4SAM to 72.5 while retaining 54.8 COCO mask AP, with no added inference operators.

On five-direction UAV oblique surveys at roughly 1 cm nadir ground sampling distance, MV-PVMapper couples the frozen vision foundation model SAM 3 with photogrammetric geometry to output georeferenced polygons for individual modules without task-specific annotation or weight updates. Across three operational rooftop PV plants holding 7,711 reference modules, two feedback rounds match 7,710 with no false positives at an IoU threshold of 0.5; macro-averaged AJI reaches 97.55%, against 60.73% for an orthomosaic segmentation baseline and 41.24% for a multi-view pixel-voting baseline.

On five-direction UAV oblique surveys at roughly 1 cm nadir ground sampling distance, MV-PVMapper couples the frozen vision foundation model SAM 3 with photogrammetric geometry to output georeferenced polygons for individual modules without task-specific annotation or weight updates. Across three operational rooftop PV plants holding 7,711 reference modules, two feedback rounds match 7,710 with no false positives at an IoU threshold of 0.5; macro-averaged AJI reaches 97.55%, against 60.73% for an orthomosaic segmentation baseline and 41.24% for a multi-view pixel-voting baseline.