## 4 Results and Discussion
### Inference Results  
Using the trained model, the land use classification reasoning of tiles was carried out, and the inference results of some cases of tiles are shown in Table 10. Combined with the remote sensing image tile data, the model classification results were visualized and analyzed in GIS to obtain the land use classification map in the study area, as shown in Figure 7. The identification results show that the proportion of building and street, public space, and green space types is high in the four urban core built-up areas. Additionally, it was observed that there is a significant presence of industrial zones and agricultural land on the periphery of these urban core areas. Moreover, another finding of this study was that certain areas had mixed-use development with a mix of residential buildings and commercial establishments. In summary, Table 11 presents the proportion of each refined urban land use classification for four urban core areas using a combination of advanced GIS techniques combined. This information provides valuable insights for policymakers and urban planners to make informed decisions regarding sustainable development initiatives within these urban regions. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Land Use Category**} & \multicolumn{3}{c}{**Metric**} \\ \cline{2-4}  & **Precision** & **Recall** & **F1-Score** \\ \hline building & 0.979 & 0.950 & 0.964 \\ building\_greenland & 0.971 & 1.000 & 0.985 \\ building\_street & 0.854 & 0.880 & 0.867 \\ construction\_vacant & 0.885 & 0.929 & 0.906 \\ greenland & 0.979 & 0.960 & 0.969 \\ greenland\_street & 0.789 & 0.750 & 0.769 \\ greenland\_street\_parking & 0.755 & 0.808 & 0.780 \\ parking & 0.889 & 0.808 & 0.847 \\ public space & 0.955 & 0.867 & 0.909 \\ railway & 0.902 & 0.920 & 0.911 \\ river\_lake\_square & 1.000 & 1.000 & 0.998 \\ road\_street & 0.750 & 0.750 & 0.750 \\ sport & 1.000 & 1.000 & 0.998 \\ street\_parking & 0.752 & 0.812 & 0.781 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Precision, Recall, and F1-Score of Xingtai City data set by category. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Land Use Category**} & \multicolumn{3}{c}{**Metric**} \\ \cline{2-4}  & **Precision** & **Recall** & **F1-Score** \\ \hline building & 1.000 & 0.988 & 0.989 \\ building\_greenland & 0.883 & 0.980 & 0.929 \\ building\_street & 0.851 & 0.970 & 0.907 \\ construction\_vacant & 0.952 & 1.000 & 0.976 \\ greenland & 0.938 & 0.891 & 0.914 \\ greenland\_street & 0.710 & 0.863 & 0.779 \\ greenland\_street\_parking & 0.774 & 0.650 & 0.707 \\ parking & 1.000 & 0.828 & 0.906 \\ public space & 0.976 & 0.837 & 0.901 \\ railway & 1.000 & 0.940 & 0.969 \\ river\_lake\_square & 0.952 & 1.000 & 0.976 \\ road\_street & 0.818 & 0.735 & 0.774 \\ sport & 0.962 & 1.000 & 0.980 \\ street\_parking & 0.667 & 0.714 & 0.690 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Precision, Recall, and F1-Score of Tangshan City data set by category. Although the introduction highlights the development of a new data set for urban land use classification in northern China, the methodology and framework of the data set are universal. It is based on widely recognized land use classification standards and combines Geographic Information System (GIS) and remote sensing technology, both of which are universal technologies and methods worldwide. Therefore, in theory, this data set development method can be transplanted and applied in other regions or countries. However, there are differences in natural environment, economic development level, urban structure, and cultural background among different region or countries, which may affect the direct transplantation of data sets. To be applied in other regions or countries, the following factors need to be considered: First, natural conditions such as climate and topography in different regions may influence land use patterns, so the development of data sets should be adjusted according to local actual conditions.