Native-Resolution Readout and Support-Centered Geographic Conditioning for Diffusion Cloud Removal

Auxiliary observations provide complementary information for cloud removal, but their utility depends on how spatial structure is represented at reconstruction. Spatial coarsening can suppress fine variation in continuous observation features; categorical maps require class composition and missing coverage to be represented distinctly. We view these as two forms of conditioning-representation mismatch and introduce native-resolution readout and support-centered geographic conditioning for diffusion-based cloud removal. Native-resolution readout exposes joint cloudy-optical and synthetic aperture radar (SAR) features directly to residual reconstruction, retaining spatial variation beyond an existing multiscale conditioning pathway. Geographic conditioning encodes land use and land cover (LULC) as target-grid class-area fractions with explicit coverage. A support-centered transformation removes the patch-average class composition while retaining within-patch spatial deviations; these features combine with SAR and elevation to update an RGB reconstruction embedding. Matched controls isolate representation effects in a 13-band pixel-space system on SEN12MS-CR and a latent-space RGB system on CloudRGB. With aligned auxiliary SAR channels, preserving the native field improves peak signal-to-noise ratio (PSNR) by 1.323 dB and reduces learned perceptual image patch similarity (LPIPS) by 0.0785 relative to spatially pooled conditioning. With geographic inputs fixed, support-centering improves full-image PSNR by 1.09 dB and reduces LPIPS by 0.0227 over uncentered area fractions in controlled CloudRGB gap reconstruction. These results support conditioning representations that preserve native spatial variation and explicitly encode valid support and local categorical deviations.