## 4 Results and Discussion
### Model Confusion Matrix Analysis  
In addition to using a confusion matrix for evaluating model performance, it is important to consider the impact of its results. The confusion matrix provides valuable insights into the classification errors across different categories and helps in assessing the level of confusion between them. By visually representing the distribution of misclassifications, it offers a comprehensive view of how well the model is performing. Furthermore, when analyzing the n \(\times\) n matrix recording image, where each number represents the probability of being classified as a certain category. As illustrated in the confusion matrix diagram, the similarity of visual features between different categories can result in inaccurate categorization, with some categories being particularly susceptible to confusion. For instance, distinguishing between green space and street or parking areas may pose challenges due to their shared visual characteristics. This similarity can result in misclassifications and contribute to errors between predicted and actual categories. The standardized confusion matrix derived from recognition results plays a crucial role in identifying this potential error. It allows for a deeper understanding of where misclassifications are occurring and highlights areas for improvement in model training or feature engineering. Ultimately, the insights derived from the confusion matrix analysis illustrated in Figure 6 can be employed to inform decisions regarding the refinement of the model's performance and the enhancement of its capacity to accurately classify images across diverse categories.  
Figure 6: Confusion matrix. (**a**) Handan City data set; (**b**) Shijiazhuang City data set; (**c**) Xingtai City data set; (**d**) Tangshan City data set.  
For EfficientNetV2 model, the accuracy, precision, recall, and F1-score are calculated by category and population. As the average of all categories, the classification results of each category are shown in Tables 6-9. Because of the different landforms and structures of each city, the classification results will be different. The training results of the model in Handan City Data set show that the precision of construction_vacant and greenland_street_parking is the highest, while that in Shijiazhuang City Data set shows that the precision of construction_vacant is the highest, that in Xingtai City Data set is river_lake_square and sport, and that in Tangshan City Data set is building, parking, and railway. Because F1-score contains both accuracy and recall scores, it is an important consideration when evaluating the classification performance. In Handan data set, the F1-Score of construction_vacant, railway, and river_lake_square is the highest, with 0.998, 0.990, and 0.942, respectively. In the data set of Shijiazhuang City, the F1-Score of construction_vacant, sport, and river_lake_square is the highest, with 0.956, 0.952, and 0.948, respectively. In Xingtai data set, the F1-Score of river_lake_square, sport, and building_greemland is the highest, with 0.998, 0.998, and 0.985, respectively. In Tangshan data set, the F1-Score of building, sport, and river_lake_square is the highest, with 0.989, 0.980, and 0.976 respectively.