## 2 Materials and Methods
### Implementation and Evaluation  
The experiments were conducted in Python (3.10.15) with PyTorch (2.4.1). We used the Adam optimizer throughout training, with an initial learning rate of \(1\times 10^{-3}\); whenever the validation performance plateaued for several epochs, the PyTorch ReduceLROnPlateau scheduler reduced the learning rate by a factor of 0.1. The batch size (32) and the number of epochs (100) were, likewise, fixed. Data augmentation, including rotations at 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\), as well as horizontal and vertical flips, was applied after patch extraction, exposing the network to multiple orientations. Figure 8: Inference pipeline using patch-wise softmax prediction and product-based aggregation.As described in the text, patches are extracted in the same way as FPE, and the patch regions are indicated by orange boxes. To compare training schemes rather than network architectures, we fixed the model architecture to EfficientNet-B0 [49]. EfficientNet has demonstrated strong performance across various remote sensing tasks, making it a suitable choice for such applications [50; 51; 52]. The model was initialized using ImageNet-1k pretrained weights, which have proven effective in remote sensing classification tasks in prior studies [53]. After introducing CBS, we maintained a total number of training iterations identical to that of the baseline, ensuring the same training steps for each approach. Similarly, RPE was aligned with the baseline settings for the number of patches extracted from each parcel and the same 10% valid-area threshold as FPE. If a patch's valid area fell below that threshold, we simply drew a new \((x,y)\) offset (up to ten attempts) and, if still invalid, defaulted to a parcel-centered patch to preserve the required patch count. This measure ensures that no confounding factors, aside from the training scheme itself, could influence the results. Model performance was primarily evaluated using accuracy, macro F1, and the Kappa coefficient [54]. Accuracy reflects how many predictions are correct, and it is primarily influenced by the majority classes in the evaluated dataset. Macro F1 computes the unweighted average of F1 scores across all classes, enabling a balanced evaluation, regardless of class distribution. Unlike simple accuracy, the Kappa coefficient provides a chance-corrected measure of classification performance, offering a more precise assessment of agreement between predicted and true labels. In addition, we supplemented the evaluation with class-wise F1 scores for each class. Particular attention was given to minority classes such as ginseng and tree nursery. To account for computational efficiency during training, we also tracked the average training time per epoch and monitored memory usage under each configuration. We also employed a validation set (Val) from the same domain (year/region) as the training set and a test set (Test) from a different domain to assess both in-domain and out-of-domain generalization. Since tiling-based methods are only applicable to models with fixed input sizes, we varied only CBS and RPE while keeping the EfficientNet-B0 backbone fixed. This approach isolated the effect of the proposed patch-based methods, ensuring that performance changes were due to the data processing techniques rather than modifications to the network architecture. Additionally, we investigated how enlarging the input parcels influences classification performance. In this study, we define the concept of patch coverage as the spatial area that each individual patch actually contains. Tiling is often adopted in resource-constrained environments where entire images cannot be processed at once due to memory limitations, resulting in a reduced patch-level context [55]. In tiling-based classification, this leads to decreased contextual information per patch while increasing the number of patches, thereby enhancing the aggregation effect.