## 5 Conclusions  
Areal settlements' shape classification is a classical and challenging issue in large-scale topographic maps. This study proposed a graph convolutional neural network shape classification method based on the skeleton lines of the areal settlement. The method uses a skeleton line of areal settlement elements as the basis for constructing the calculation graph. The geometric features of the skeleton line segments are extracted as the nodal attribute features of the computational graph, using the skeleton line segments as the smallest unit to express the shape features of the faceted areal settlement in a graph structure. To achieve shape classification of an areal settlement, a graph with labels and node attributes is used as input for the GCN classification mode, and the graph convolutional neural network is used to discriminate between high-dimensional shape features through supervised training. Lastly, a fully connected classifier can accurately classify the areal settlement's shape.  
Figure 14: Experimental data of Lanzhou City.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & **Artificial** & **Number of** & **Number of** & \multirow{2}{*}{**P/(\%)**} & \multirow{2}{*}{**R/(\%)**} & \multirow{2}{*}{**F**} \\  & **Number of** & **Classifications** & **in the Model** & & & \\ \hline I-shape & 88 & 84 & 84 & 100 & 95.5 & 97.7 \\ L-shape & 40 & 42 & 40 & 95.2 & 100 & 97.5 \\ T-shape & 17 & 12 & 11 & 91.7 & 64.7 & 75.9 \\ U-shape & 38 & 38 & 36 & 94.7 & 94.7 & 94.7 \\ Z-shape & 9 & 14 & 8 & 57.1 & 88.9 & 69.5 \\ E-shape & 15 & 14 & 14 & 100 & 93.3 & 96.5 \\ Y-shape & 6 & 9 & 5 & 55.6 & 83.3 & 66.7 \\ F-shape & 13 & 13 & 12 & 92.3 & 92.3 & 92.3 \\ H-shape & 14 & 14 & 14 & 100 & 100 & 100 \\ total & 240 & 240 & 224 & 93.3 & 93.3 & 93.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classified statistics of some settlements in Lanzhou.  
The experimental results show that the method has a high discriminative ability for areal settlement shapes.  
The method uses areal settlement skeleton lines as input data to construct an end-to-end classification model. Validation with real data sets has demonstrated that the skeleton line can effectively represent both local and global features and has a high discriminative ability for the shape of alphabetical areal settlement. The distinction of complex shapes of areal settlement such as F- and E- and H-types is particularly effective and outperforms other existing methods. However, the objects of the areal settlement in this method are large-scale. Shape variation in small- and medium-scale areal settlements is one of the main areas to be improved and is proposed to be studied in depth in combination with the method of graph convolutional neural networks.  
[PERSON] and [PERSON] conceived the skeleton-line and Graph Convolutional Neural Network together; [PERSON] collected the data, designed the experiments, and wrote the manuscript; [PERSON] and [PERSON] offered significant contribution to result evaluation; [PERSON] modified the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 41930101, 42161066), the Industrial Support and the Program Project of Universities in Gansu Province (grant no.2022 CYZC-30).  
Not applicable.  
The authors declare no conflict of interest.