# A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification
## 4 Discussion  
In our testing, MobileNet achieved a high accuracy score of 0.9500 within just 152.71 s of training on our machine, making it the fastest among the 39 models evaluated. However, it ranked only 26 th in terms of accuracy. MobileNet is a lightweight deep neural network intentionally designed with fewer parameters than other models, yet it still maintains high accuracy in image classification tasks. This feature has made it a popular choice for real-time image recognition applications on devices with limited computational resources, such as mobile phones or embedded systems. The architecture of MobileNet includes depthwise separable convolutions, which reduce the computational cost of convolutions used during the classification process. This feature allows the network to process large amounts of data efficiently while maintaining high accuracy. While ResNet50 had the highest overall accuracy score, it ranked 23 rd in terms of training time. ResNet152 achieved the third highest accuracy score, but it ranked 32 nd in training time. Both models belong to the ResNet family of deep neural networks. ResNet50 has 50 convolutional layers and the ability to learn intricate patterns while overcoming vanishing gradients. On the other hand, ResNet152 has 152 layers and uses skip connections to directly transmit information between layers, solving the vanishing gradient problem during deep neural network training. EfficientNetV2B0 achieved a high accuracy score of 0.9716, ranking second overall, but it required 2.4 times longer training time than MobileNet. EfficientNetV2B0 is an upgraded version of the original EfficientNet model, which is designed to be computationally efficient while maintaining high accuracy in image classification tasks. Overall, the EfficientNetV2 and RegNet models showed relatively high kappa values in relation to training time compared to the other 39 models evaluated. Table 3 provided [PERSON]'s kappa, cross-entropy loss, and accuracy metrics for the ResNet50, EfficientNetV2B0, and ResNet152 models. Among these models, the maximum difference in kappa scores on the test set was 0.0082, and the maximum difference in accuracy scores was 0.0074. However, since the EuroSAT dataset is not well balanced across all categories, the f1-score might be a better performance indicator than accuracy [70]. According to Table 4, EfficientNetV2B0 achieved the highest f1-scores in the Annual Crop, Highway, Permanent Crop, and River categories. ResNet50 performed best in the Forest, Herbaceous Vegetation, Industrial, Pasture, and Sea Lake classes, while ResNet152 achieved the highest f1-score only in the Residential category. Moreover, since EfficientNetV2B0 required less training time than ResNet50, it achieved impressive f1 scores. The performance of the models in different categories was further validated by the confusion matrices on the test set. The results were consistent with the f1-scores reported in Table 4. For instance, EfficientNetV2B0 showed the least confusion in classifying the Permanent Crop category, while ResNet152 performed best in the Herbaceous Vegetation, Highway, and Residential classes. ResNet50 exhibited strong performance in the Annual Crop, Forest, Industrial, Pasture, and River categories. By splitting the EuroSAT dataset into train and test sets with 80% and 20% distributions, respectively, [PERSON] et al. [35] obtained an overall accuracy of 0.9874. [PERSON] et al. [31] achieved a total accuracy of 0.9857 by dividing the EuroSAT dataset into a training (80%) set and a test (20%) set. [PERSON] et al. [12] segmented the EuroSAT into two sets, the train set (75%) and the test set (25%), which was used for both validations and testing, and obtained an overall accuracy of 0.9917. However, we divided the EuroSAT dataset into three subsets, train (80%), validation (10%), and test (10%), and kept the test set unseen by models for testing purposes. Our best overall accuracy was obtained using ResNet50, 0.9751, which is the lowest of any of the research listed.