## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline
**Author** & **Data Source** & **Models** & **Application** \\ \hline \multirow{4}{*}{[PERSON] et al. (2017) [39]} & \multirow{4}{*}{Deep brief network} & Multispectral SPOT-5 and & \multirow{4}{*}{Urban land use and vegetation} \\  & & Landsat images, & \\  & & Google earth images & \\ \hline [PERSON] et al. (2019) & \multirow{4}{*}{ImageNet data set} & CNN-CapsNet & Scene classification \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \multirow{4}{*}{[PERSON] et al. (2020) [27]} & \multirow{4}{*}{ZY-3 and GF-2 satellites} & Hybrid convolutional feature extraction module & Feature Extraction Module for change detection based on multi-sensor remote sensing images \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \multirow{4}{*}{[PERSON] et al. (2022) [12]} & \multirow{4}{*}{Google Earth images} & TR-CNN & \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \multirow{4}{*}{[PERSON] et al. (2022) [43]} & GIS data to produce a & \multirow{4}{*}{DUA-Net} & Land use classification \\  & well-tagged and & & \\ \cline{1-1}  & high-resolution urban & & \\ \cline{1-1}  & land-use image data & & \\ \cline{1-1}  & set & & \\ \hline \multirow{4}{*}{[PERSON] et al. (2023) [14]} & Sentinel-2 satellite images, OSCD data set & EfficientNetV2 T-Unet, & Urban change detection \\  & & set & Semi transfer learning & \\ \hline \multirow{4}{*}{[PERSON] and [PERSON] (2023) [16]} & \multirow{4}{*}{EuroSAT data set} & ResNet50, & \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \multirow{4}{*}{[PERSON] et al. (2023) [17]} & Sentinel-2 L2A & \multirow{4}{*}{EfficientNetV 2-S} & Land use/Land cover classification \\  & & imagery & \\ \hline \multirow{4}{*}{[PERSON] et al. (2020) [40]} & \multirow{4}{*}{Sentinel-2 multispectral data} & A deep convolutional neural network composed of residual learning and the Squeeze-and-Excitation block, namely the LCZNet & Land use classification \\  & & & \\ \cline{1-1}  & & & \\ \hline \multirow{4}{*}{[PERSON] et al. (2020) [44]} & \multirow{4}{*}{OSCD} & Multidimensional CNN & Urban land use land cover \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Literature review of land use classification based on the deep learning model. green vegetation on both sides, and the artificial classification has fuzzy judgment [4; 46]. In comparison with the advantages of foreign urban land classification, such as clear boundaries, accurate road delineation, and easy identification, it is evident that foreign data sets cannot be directly applied to land classification in China. At present, the commonly used scene recognition data sets include SIRI-WHU data set [47], WHU-RS19 data set [48], and UC Merced Land Use data set [49]. These data sets encompass categories like desert, forest, beach, and so on. Due to the differences between foreign data sets and domestic land use status, the applicability of these foreign data sets is limited, and they cannot be directly used for image recognition of urban land use classification in China. Therefore, it is necessary to establish a remote sensing image data set for local land use classification task. The main research objectives of this paper are as follows: (1) to construct a data set based on the characteristics of land use classification in the urban core area of Chinese northern cities. (2) The core built-up areas of four typical cities, namely Handan, Shijiazhuang, Tangshan, and Xingtai, are selected to construct a three-channel satellite remote sensing image data set and manually classified into 14 categories. (3) By using the convolutional neural network EfficientNetV2 model to realize the intelligent classification of urban land remote sensing images.