# A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**(a) ResNet50**} \\ \hline  & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Validation**} & \multicolumn{2}{c}{**Test**} \\ \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline Annual Crop & 0.996 & 0.989 & 0.992 & 0.987 & 0.954 & 0.970 & 0.967 & 0.964 & 0.966 \\ Forest & 0.995 & 0.997 & 0.996 & 0.990 & 0.983 & 0.986 & 0.986 & 0.993 & 0.989 \\ Herbaceous & 0.990 & 0.983 & 0.986 & 0.980 & 0.964 & 0.972 & 0.975 & 0.954 & 0.964 \\ Vegetation & & & & & & & & \\ Highway & 0.986 & 0.982 & 0.984 & 0.933 & 0.925 & 0.929 & 0.948 & 0.916 & 0.932 \\ Industrial & 0.996 & 0.993 & 0.995 & 0.992 & 0.964 & 0.978 & 0.988 & 0.981 & 0.984 \\ Pasture & 0.981 & 0.989 & 0.985 & 0.951 & 0.947 & 0.949 & 0.974 & 0.964 & 0.969 \\ Permanent Crop & 0.983 & 0.989 & 0.986 & 0.920 & 0.982 & 0.950 & 0.933 & 0.950 & 0.941 \\ Residential & 0.996 & 1.000 & 0.998 & 0.980 & 0.997 & 0.988 & 0.987 & 0.984 & 0.986 \\ River & 0.980 & 0.988 & 0.984 & 0.929 & 0.943 & 0.936 & 0.918 & 0.966 & 0.941 \\ Sea Lake & 0.999 & 0.998 & 0.999 & 0.987 & 0.997 & 0.992 & 0.990 & 1.000 & 0.995 \\ Accuracy & 0.991 & 0.991 & 0.991 & 0.967 & 0.967 & 0.967 & 0.968 & 0.968 & 0.968 \\ Macro avg & 0.990 & 0.991 & 0.991 & 0.965 & 0.966 & 0.965 & 0.967 & 0.967 & 0.967 \\ Weighted avg & 0.991 & 0.991 & 0.991 & 0.967 & 0.967 & 0.967 & 0.968 & 0.968 & 0.968 \\ \hline  & \multicolumn{8}{c}{**(b) EfficientNetV2B0**} \\ \hline  & \multicolumn{8}{c}{**Train**} & \multicolumn{2}{c}{**Validation**} & \multicolumn{2}{c}{**Test**} \\ \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline Annual Crop & 0.981 & 0.988 & 0.984 & 0.973 & 0.980 & 0.976 & 0.989 & 0.951 & 0.970 \\ Forest & 0.983 & 0.991 & 0.987 & 0.983 & 0.986 & 0.984 & 0.976 & 0.976 & 0.976 \\ Herbaceous & 0.989 & 0.939 & 0.964 & 0.984 & 0.940 & 0.961 & 0.975 & 0.920 & 0.946 \\ Highway & 0.975 & 0.972 & 0.973 & 0.944 & 0.944 & 0.944 & 0.944 & 0.933 & 0.939 \\ Industrial & 0.992 & 0.995 & 0.993 & 0.975 & 0.988 & 0.981 & 0.977 & 0.984 & 0.980 \\ Pasture & 0.965 & 0.968 & 0.967 & 0.966 & 0.952 & 0.959 & 0.948 & 0.948 & 0.948 \\ Permanent Crop & 0.953 & 0.983 & 0.968 & 0.932 & 0.982 & 0.957 & 0.903 & 0.984 & 0.941 \\ Residential & 0.992 & 0.997 & 0.995 & 0.993 & 0.993 & 0.993 & 0.990 & 0.990 & 0.990 \\ River & 0.977 & 0.981 & 0.979 & 0.936 & 0.933 & 0.934 & 0.947 & 0.955 & 0.951 \\ Sea Lake & 0.994 & 0.996 & 0.995 & 0.993 & 0.993 & 0.993 & 0.977 & 1.000 & 0.988 \\ Accuracy & 0.981 & 0.981 & 0.981 & 0.970 & 0.970 & 0.970 & 0.964 & 0.964 & 0.964 \\ Macro avg & 0.980 & 0.981 & 0.980 & 0.968 & 0.969 & 0.968 & 0.962 & 0.964 & 0.963 \\ Weighted avg & 0.981 & 0.981 & 0.981 & 0.970 & 0.970 & 0.970 & 0.965 & 0.964 & 0.964 \\ \hline  & \multicolumn{8}{c}{**(c) ResNet152**} \\ \hline  & \multicolumn{8}{c}{**Train**} & \multicolumn{2}{c}{**Validation**} & \multicolumn{2}{c}{**Test**} \\ \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline Annual Crop & 0.989 & 0.987 & 0.988 & 0.956 & 0.966 & 0.961 & 0.953 & 0.943 & 0.948 \\ Forest & 0.994 & 0.996 & 0.995 & 0.976 & 0.989 & 0.983 & 0.990 & 0.979 & 0.984 \\ Herbaceous & 0.981 & 0.984 & 0.983 & 0.967 & 0.964 & 0.966 & 0.950 & 0.962 & 0.956 \\ Vegetation & & & & & & & & \\ Highway & 0.978 & 0.981 & 0.979 & 0.937 & 0.915 & 0.925 & 0.933 & 0.940 & 0.936 \\ Industrial & 0.997 & 0.984 & 0.990 & 0.992 & 0.972 & 0.982 & 0.988 & 0.966 & 0.977 \\ Pasture & 0.991 & 0.983 & 0.987 & 0.956 & 0.952 Figure 4: Normalized confusion matrix for ResNet50 (**left**), EfficientNetV2B0 (**center**), and ResNet152 (**right**) on the test set. Figure 5: ResNet50 is used to predict a sample from the test set. Correctly predicted labels are displayed in green text, whereas incorrectly predicted labels are indicated in red text.