## 1 Introduction
In contrast, fixed-size patch extraction avoids this problem by directly sampling from the original image without altering its resolution, as shown in Figure 1c. This method preserves the local spatial scale and textural characteristics of each parcel, enabling the model to capture more reliable and class-specific visual cues, thereby improving its ability to distinguish between visually similar classes. To systematically implement this fixed-size patch approach, tiling-based classification frameworks have been introduced [42; 43]. Instead of resizing the entire parcel, this approach divides each parcel into overlapping fixed-size patches. Each patch is individually classified by a CNN, and parcel-level predictions are aggregated from patch-level results--typically using a class-wise product. This strategy maintains spatial resolution, avoids global scaling distortions, and satisfies CNN input requirements, making it a widely adopted approach in tiling-based classification. Despite these advantages, typical tiling-based training methods have limitations. First, the Fixed Patch Extraction (FPE) method crops patches from fixed spatial offsets, which matches inference-time behavior. However, this fixed sampling strategy restricts spatial diversity in training data, as the same patch locations are repeatedly used during training epochs, thereby limiting variation in spatial contexts [44; 45]. Such structural limitations can lead to overfitting to specific spatial patterns and degrade generalization performance for unseen domains [44]. Although repeated patch exposure is somewhat reduced by randomly sampling only 40% of patches when more than three patches are generated per parcel, fixed offsets still cause patch duplication across epochs. This redundancy limits training diversity and heightens the risk of overfitting, as shown in Figure 2.  
Second, most tiling-based pipelines preserve the original class distribution without applying balancing mechanisms--a scenario we define as No Class Balancing (NCB). In imbalanced farmland datasets, NCB results in skewed training exposure: majority classes dominate the training process, while rare classes (e.g., ginseng or tree nursery) become severely under-represented. Consequently, models develop biases toward frequent classes, performing poorly on minority classes. In short, patch duplication from FPE and class imbalance due to NCB degrade the generalization capability of models, underscoring the need for more robust training strategies tailored to parcel-based tiling classification. Figure 1: Comparison between fixed-size patch extraction and resizing as input preparation strategies. (**a**) Orchard sample (1 m/pixel). (**b**) Growing field sample (1 m/pixel). (**c**) Fixed-size patch comparison (1 m/pixel). (**d**) Resized image comparison (orchard: 4.5 m/pixel; growing field: 1.6 m/pixel) (**e**) Zoomed-in view of fixed-size patch. (**f**) Zoomed-in view of resized image. To address these challenges, we adopt two complementary techniques: random patch extraction (RPE) and class-balanced sampling (CBS). RPE improves overall classification performance by ensuring spatial diversity in training patches and also enhances performance across most minority classes. Under the 1\(\times\) resolution setting, overall accuracy increased by +0.0176 on the validation set and +0.0574 on the test set. For an easy minority class, F1 scores improved by +0.0696/+0.0790 (Val/Test), respectively. However, when used alone, RPE does not resolve class-level training imbalance. In very difficult minority classes, the spatial diversity provided by RPE hindered generalization under limited training exposure, and the model failed to make any correct predictions, with F1 scores dropping to 0 on both the validation and test sets. CBS mitigates majority-class bias by equalizing class-level training opportunities and facilitates better inter-class discrimination, improving both overall and minority-class performance.