Cross-Domain Mixup for Parcel-Level Crop Mapping on a Multi-Year Sentinel-2 Dataset from Slovakia

Reliable crop-type mapping from satellite image time series is affected by distribution shifts across geographic regions, agricultural years, and heterogeneous label systems. To address this challenge, we propose Cross-Domain Mixup (CDMix), a supervised domain-adaptation method designed to leverage a larger labeled source dataset to improve performance on a smaller labeled target dataset under distribution shifts. We also introduce PixelSet-Slovakia, a new multi-year, parcel-level Sentinel-2 dataset covering three Slovak study regions and several growing seasons. Using a common backbone, we compare CDMix against three families of adaptation strategies: (i) no adaptation, (ii) weight transfer through fine-tuning and encoder freezing, and (iii) feature-space alignment using Maximum Mean Discrepancy (MMD) and Correlation Alignment (CORAL). All methods are evaluated in two scenarios: geographic supervised adaptation across datasets from two countries and temporal supervised adaptation across different growing seasons. Across both tested source–target settings, CDMix generally achieves competitive performance when initialized from pretrained representations. Under the region-held-out validation protocol, several pretrained adaptation strategies outperform training from scratch.