# Optimizing Mobile Vision Transformers for Land Cover Classification
## 4 Results
The class-based performance for the 60-40 AID testing dataset is shown in Table 5. The SWIN-Reg model and its variants, while not outperforming the MViT series, still offered a different approach to model architecture, emphasizing regularization and compactness with only 155,022 parameters for SWIN-Reg itself. SWIN-ShuffleNet, another variant, showed an interesting combination of SWIN's approach with ShuffleNet's efficiency. The per-class validation accuracy of the 60-40 split on the AID dataset showed significant improvements. For example, the accuracy for Airports(class 0) jumped from 40% to 86.81% for MViT-Combined. Similar trends were observed for other classes, indicating that the MViT variants can provide substantial accuracy benefits over the baseline ViT model. However, challenges persisted in classifying Squares (class 26), which could be due to the presence of various sub-classes within the category. Loss functions reported a consistent trend for both the training and testing data, as shown in Figure 7.  
The findings from the EuroSAT experiment group demonstrated that the highest accuracy was uniformly distributed across all types of splits, as indicated in Table 6. The MViT-Combined model demonstrated strong performance on the EuroSAT dataset, achieving a test accuracy of 97.8% on the 40-60 split with just 1,010,105 parameters. Unlike with the AID dataset, where shifts from ViT to MViT variants manifested noticeable improvements, the EuroSAT dataset saw less significant increases in class accuracy upon implementing advanced models. Despite the modest relative value changes, the absolute values of the MViT variants were notably high. This is particularly evident in the case of Residential (class 6), where all MViT variants achieved 100% class accuracy. Beyond Residential, several other classes also benefited from the advanced architecture. For example, the accuracies of Highways (Class 3) and Industrial (Class 4) show significant increases from 82.4% and 93.27% in ViT to 95.67% and 95.73%, respectively, in MViT-Combined models. The SWIN-Reg model's accuracy of 89.23% on the 40-60 split is notable in terms of parameter efficiency. The SWIN-ShuffleNet variant further underscores the efficacy of combining architectures, achieving 92.07% on the 40-60 split. The advancements in EuroSAT's class accuracies warrant further analysis to understand the underlying factors contributing to these results. Training loss outcomes can be seen in Figure 8.  
Figure 6: Mean testing accuracy graphs of the best models evaluated in this study. The results for the UC-Merced experiment group show that the 50-50 split model typically performed exceptionally well, with the MViT-Combined variant achieving the highest test accuracy of 92.1% on this split. In this configuration, the MViT-Depth variant reached an accuracy of 91.71%, significantly outperforming ViT's best split by over 16% and outdoing the benchmark MViT's best split by nearly 4.09%. All MViT variants surpassed the ViT model across each evaluated split. The impressive performance of these models, particularly MViT-Combined, suggests an optimal balance between the transformer architecture's capabilities and the efficiency needed for mobile deployment, positioning them as a scalable solution for high-resolution satellite imagery analysis. The class-based performance for the 50-50 test split is detailed in Table 7. The MViT architecture contributed to substantial improvements across multiple classes, with notable advances in distinguishing different types of urban structures. For example, in Classes 2, 9, 12, and 19, we noticed a significant improvement for MViT-Combined compared to the baseline MViT. Baseball Diamond (Class 2) saw scores increase from 88% to 96%, while Golf Course (Class 9) scores increased from 80% to 100%. Medium Residential  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & **ViT** & **MViT** & **MViT-Depth** & **MViT-Combined** & **SWIN-Reg** & **SWIN-Conv** & **SWIN-ShuffleNet** \\ \hline
**0** & 40.