# A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification
## 3 Results  
All models were trained in accordance with the descriptions in Section 2.2. Each model was trained and validated using train and validation sets, respectively. The test set was placed aside for assessments. Then, using the train, validation, and test sets for each model, the cross-entropy loss and accuracy, as defined in Equation (3) were computed, and the average of these three values for each model can be seen in Table 2. ResNet50, EfficientNetV2B0, and ResNet152 demonstrated the highest overall accuracy metrics, as seen in Table 2. Moreover, all models were trained using TensorFlow-GPU v2.10.0 [56] on a Microsoft Windows 11 PC equipped with an AMD Ryzen 9 3900X 12-core processor, 32 GB RAM (DDR4-3600), NVIDIA RTX2080 Super GPU. The training time for each of the models utilized in this study is exhibited in Table 2. As can be observed, mobile networks typically require shorter training time [43]. On our test machine, ResNet152 took nearly three times as long to train as EfficientNetV2B0, while ResNet50 took nearly twice as long as EfficientNetV2B0. According to [PERSON] et al. [42], some of the EfficientNetV2 models were expected to have a lower overall training time. During our experiments, EfficientNetV2 models outperformed EfficientNet models in terms of accuracy and training time. [PERSON]'s kappa, cross-entropy loss, and accuracy were calculated for the ResNet50, EfficientNetV2B0, and ResNet152 models. Unlike many previous transfer learning models developed on the EuroSAT dataset, our models never encountered the test set throughout the training and validation processes. This provides a key metric for evaluating these models. As seen in Table 3, ResNet50 has the highest accuracy metrics. On the Test set, ResNet50 achieved a kappa score of 0.9641 and an accuracy score of 0.9678. For ResNet50, EfficientNetV2B0, and ResNet152 models, accuracy, recall, and f1-scores were computed by class and overall, as the average of all classes. Table 4 summarizes these metrics. On the train set, ResNet50 and ResNet152 have slightly higher overall f1-scores than EfficientNetV2B0. However, in the validation and test sets, ResNet50 and EfficientNetV2B0 narrowly outperform ResNet152 in terms of f1-score. Because the f1-score contains both accuracy and recall scores, it is an important metric to consider when evaluating classification performance. As Table 4 indicates, the f1-score on the test set for ResNet50, EfficientNetV2B0, and ResNet152 models are \(0.967\pm 0.021\), \(0.963\pm 0.019\), and \(0.959\pm 0.024\), respectively. ResNet50 achieved an overall f1-score of 0.967 on the test set with the lowest score in the Highway class and the highest in the Sea Lake class. According to Table 4, EfficientNetV2B0 had the highest f1-score in the Annual Crop, Highway, Permanent Crop, and River classes, whereas ResNet50 had the best f1-score in the Forest, Herbaceous Vegetation, Industrial, Pasture, and Sea Lake classes. ResNet152 was only the best in the Residential class. The test set confusion matrices for ResNet50, EfficientNetV2B0, and ResNet152 are illustrated in Figure 4. As seen in Figure 4, the Highway class was the most confusing for ResNet50 in the test set, whereas Sea Lake was the least puzzling. EfficientNetV2B0 had the least confusion in classifying the Permanent Crop class, whereas ResNet152 performed the best in the Herbaceous Vegetation, Highway, and Residential classes. ResNet50 excelled in the classifications of Annual Crop, Forest, Industrial, Pasture, and River classes. A random subset of the test set was sampled, and predictions were made using the ResNet50 model. Figure 5 depicts these predictions, and Table 5 additionally includes the probability for predicted classes. The ResNet50 model erroneously predicted an image from the Pasture class as an image from the Highway class. As shown in Table 5, the image was predicted to be from the Highway class with a probability of 0.946, the Herbaceous Vegetation class with a probability of 0.014, and the Pasture class with a probability of 0.025.