SCALAR: Scale Anchoring and Label-Free Recalibration for Cross-Sensor Landslide Mapping Between UAV and Satellite Imagery

Cross-sensor landslide mapping is limited by differences in image resolution and unreliable target pseudo-labels. We present SCALAR (Scale Anchoring and Label-Free Recalibration), an unsupervised adaptation workflow built on an exponential-moving-average teacher and ClassMix. GSD Anchoring (GSA) uses ground sampling distance metadata for bounded resampling. Logit-Otsu Calibration (LOC) jointly adjusts the pseudo-label boundary and relative retention margins. Ratio-Signed Regularization (RSR) makes these margins asymmetric according to the target-to-anchor GSD ratio. It is exploratory; GSA + LOC with symmetric margins is the recommended configuration. These modules introduce no trainable parameters. In the primary CAS region-held-out evaluation across five seeds, GSA + LOC increased the landslide intersection over union (IoU) by 17.9±4.5 points beyond scale anchoring when transferring satellite/aerial imagery (SAT) to unmanned aerial vehicle imagery (UAV), with no resolved additional gain in UAV-to-SAT. At seed 0, SAT-to-UAV threshold-independent average precision (AP) increased from 0.3484 for the anchored source to 0.4809 with GSA + LOC on held-out regions, compared with an increase from 0.6371 to 0.6495 under the original transductive protocol. Under that protocol, fixed-checkpoint region-cluster bootstrap supported GSA gains in both directions and LOC gains only in SAT-to-UAV; RSR’s 95% intervals included zero in both directions. UAV-to-SAT benefits varied across backbones, and negative transfer occurred in some tested settings. The findings support coordinating scale and pseudo-label decisions within CAS while identifying teacher reliability and target resolution as conditions to examine before broader deployment.