# A Transformer-Based Approach for Efficient Geometric Feature Extraction from Vector Shape Data
## 5 Conclusions  
The PSRT model proposed in this study significantly enhances the efficiency and accuracy of vector shape feature extraction through self-supervised pre-training tasks. Compared with methods that rely on statistical features and graph neural networks, PSRT reduces the dependency on manual feature design while exhibiting higher generalization and adaptability across various downstream tasks. Experimental results illustrate that in shape classification tasks, PSRT improves the average accuracy by approximately 10% and increases training efficiency by about 25 times compared to the end-to-end SRT training. In few-shot learning scenarios, the PSRT model's performance decreases by only about 8%, far superior to non-pre-trained models, with a similar 25-fold boost in training efficiency. In road matching tasks, PSRT exhibits an 8.23% increase in accuracy and more than a fivefold improvement in training efficiency compared to end-to-end SRT training. This substantial  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **SRT (End-to-End)** & **PSRT** \\ \hline Sample Nums & 1600 & 1600 \\ Train Steps & 5000 & 1000 \\ Accuracy & 87.65\% & 92.59\% \\ Precision & 95.83\% & 98.11\% \\ Specificity & 95.72\% & 95.84\% \\ Recall & 89.47\% & 91.23\% \\ F1-score & 90.57\% & 94.55\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison between SRT (end-to-end) and PSRT models.  
Figure 22: Impact of pre-training tasks on model training efficiency.  
reduction in training time and data volume requirements significantly lowers the labor costs associated with data labeling. Future research should focus on further optimizing the network architecture to satisfy the demands of feature extraction in regions that contain multiple samples and a broader array of geoinformation tasks. Overall, PSRT provides a universal and efficient solution for the automated feature extraction and analysis of vector data. Although this study focuses on geospatial applications, the approach adopted by PSRT, which utilizes transformers for sequence-based feature extraction, can be adapted to other fields that handle sequences or geometric data, such as time series analysis in finance and trajectory analysis in robotics, given a well-designed pre-training task.  
Conceptualization, L.C. and H.Q.; Data curation, X.N. and X.W.; Formal analysis, L.C. and J.X.; Methodology, L.C., X.N. and H.Q.; Software, L.C.; Supervision, H.Q.; Validation, L.C.; Writing--original draft, L.C.; Writing--review and editing, L.C., H.Q., X.W. and J.X. 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, grant numbers 42271463, 42101453.  
Not applicable.  
The building data were downloaded from OpenStreetMap. The datasets and code are available by contacting the first author.  
The authors would like to thank their supervisors for their guidance and support during this study, and the reviewers for their constructive comments on earlier versions of the manuscript.  
The authors declare no conflicts of interest.