Automated mapping of driftwood deposits along Arctic coastlines is a challenging task due to spectral ambiguity, strong depositional heterogeneity, and limited training data. Systematic comparisons of sensor modality and deep learning architecture choices remain absent for this application, leaving practitioners without evidence-based guidance on which combination to deploy. Here we present a statistical evaluation of three architectures, U-Net, Swin-U-Net, and the TerraMind foundation model, across aerial (15 cm), PlanetScope (3 m), and Sentinel-2 (10 m) imagery acquired over ten target areas in the Mackenzie Delta, Arctic Canada. Each combination was trained ten times and evaluated within a Bayesian hierarchical framework to account for run-to-run variability inherent to stochastic training. Choice of the sensor is the dominant performance driver, having an effect approximately four times larger than the choice of architecture in Intersection over Union and a total sensor spread of 0.40 IoU between aerial and Sentinel-2 imagery. Architecture choice is of limited practical consequence at sub-metre and intermediate resolution, but becomes a first-order concern when constrained to coarse imagery: U-Net performs poorly on Sentinel-2 with a posterior mean IoU of 0.127, while transformer-based architectures show a more gradual performance decline. Swin-U-Net paired with PlanetScope imagery is the most competitive accessible alternative to aerial acquisition, with a 94 % posterior probability of practical equivalence to the top-ranked configuration. Conventional single-run evaluation missed this combination as a practical alternative, typically placing it at ranks 4–5. Probabilistic multi-run evaluation is therefore a necessary condition for reliable model selection in spectrally ambiguous remote sensing benchmarks, and the framework presented here could be directly transferable to similar Arctic mapping targets.