## 5 Conclusions  
The refined identification of urban land classification is essential for the statistical analysis of urban land and assessment of built environment quality. In this paper, we explored an automatic method for identifying urban land classification using the EfficientNetV2 model to accurately and efficiently identify multiple urban land types in remote sensing images. The research conclusions are as follows:  
1. Compared with the traditional method of field survey to identify land use, the establishment of a remote sensing image data set can effectively avoid the problems of extended time, large input of human and material resources, and low efficiency of extracting information. The fine land use identification method proposed in this study can be applied to the typical northern Chinese cities in the data set to obtain the latest urban \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Categories**} & **Handan City** & **Shijiazhuang City (\%)** & **Xingtai City (\%)** & **Tangshan City** \\ \hline building & 10.84 & 2.56 & 6.38 & 3.37 \\ building\_greenland & 5.69 & 7.37 & 5.59 & 5.76 \\ building\_street & 18.33 & 10.07 & 3.33 & 9.76 \\ construction\_vacant & 11.33 & 1.51 & 2.09 & 3.41 \\ greenland & 5.65 & 10.41 & 27.49 & 12.82 \\ greenland\_street & 6.03 & 6.35 & 8.83 & 5.68 \\ greenland\_street\_parking & 2.34 & 1.86 & 2.48 & 6.02 \\ parking & 4.3 & 18.15 & 12.27 & 16.66 \\ public space & 10.4 & 15.48 & 17.26 & 16.65 \\ railway & 10.88 & 9.62 & 4.8 & 5.6 \\ river\_lake\_square & 2.28 & 3.88 & 2.33 & 2.53 \\ road\_street & 3.27 & 1.45 & 1.36 & 2.02 \\ sport & 5.3 & 7.13 & 5.06 & 6.98 \\ street\_parking & 3.37 & 4.16 & 0.72 & 2.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The proportion of each refined urban land use classification for the four urban core areas. land use classification data in a time-efficient manner. This allows for the real-time monitoring and scientific assessment of the built-up situation of urban land use. 2. The EfficientNetV2 model is used to perform large-scale land classification inference on remote sensing images, providing technical methods for more refined quantitative analysis of the built environment, such as the greening rate of the community, the quality of the blue and green space of neighborhoods, the accessibility of sports facilities, and the occupation of parking in the public space. 3. Experiments can be completed more efficiently by utilizing the lightweight and fast nature of the EfficientNetV2 model compared to traditional convolutional neural networks. The testing set accuracy was improved after transfer learning, and the model is gradually stabilized with increased iterations. Higher accuracy of land use classification is achieved by using free civil remote sensing images, which provides a new idea and method for quantitative analysis of the urban built environment at the macro level. We will consider collecting more remote sensing satellite image data to build up data sets for training and testing so that it can be better applied to identify urban land classification in future studies. There are some limitations in this study. Firstly, there are two constraints in the artificially constructed data set. On the one hand, it is prone to be influenced by the mixture of multiple land use types during manual classification, which would lead to the confusion of the classification results. On the other hand, there might be minor differences in the definition of the same photo by different researchers, which could also constitute errors in the classification data set. Subsequently, this error can be reduced to the acceptable range after repetitive tests by multiple individuals. Secondly, due to the variations in urban structure, land use distribution, and land usage, multiple city data sets in different regions should be constructed, and large-scale data training should be carried out to improve the generalization ability of deep learning models. Conceptualization, Z.W. and X.Z. ; data curation, Y.L., Y.H., Y.C. and X.Z.