# A Registration Method for Historical Maps Based on Self-Supervised Feature Matching
## 5 Conclusions  
To address the challenges of historical map registration, we collaborated with museums and research institutions across different regions to collect and organize a dataset of historical maps from various periods and cities, using different drawing techniques. We designed a U-Net-based image segmentation model and manually annotated a large number of historical maps, which became the training and test sets of our model, removing noise such as creases and stains from the images. Next, we used the SuperPoint model, initially training a baseline feature extraction model using the generated basic shape images to extract feature points and descriptors. We then fine-tuned the model with the annotated historical map images to improve its performance on map data. To enhance the information contained in feature descriptors, we proposed a graph attention network called MapReduceFormer, allowing information to flow between feature points within and across images. We proposed a customized iterative Sinkhorn algorithm, converting the problem \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **Metrics** & **a–b** & **a–c** & **a–d** & **a–e** & **a–f** & **a–g** & **a–h** \\ \hline \multirow{4}{*}{SIFT2} & RT & 2.067 s & 2.108 s & 2.183 s & 2.019 s & 2.181 s & 2.053 s & 2.047 s \\  & MI & 0.0366 & 0.0433 & 0.0364 & 0.0345 & 0.0391 & 0.0359 & 0.0337 \\  & ROCC & 0.029 & 0.160 & 0.147 & 0.114 & 0.096 & 0.036 & 0.175 \\  & RMSE & 1.369 & 0.120 & 0.951 & 2.756 & 1.376 & 1.765 & 0.753 \\ \hline \multirow{4}{*}{SuperGlue} & RT & 5.765 s & 4.122 s & 3.199 s & 3.150 s & 3.453 s & 4.017 s & 3.240 s \\  & MI & 0.0764 & 0.0401 & 0.0339 & 0.0472 & 0.0467 & 0.0798 & 0.0614 \\  & ROCC & 0.079 & 0.325 & 0.230 & 0.112 & 0.384 & 0.197 & 0.155 \\  & RMSE & **0.030** & 0.105 & 2.151 & 3.154 & 2.025 & 0.372 & 0.236 \\ \hline \multirow{4}{*}{DeDoDe} & RT & 3.157 s & 3.544 s & 3.828 s & 2.691 s & 2.898 s & 3.606 s & 3.070 s \\  & MI & 0.0270 & 0.0206 & 0.0273 & 0.0253 & 0.0210 & 0.0414 & 0.0372 \\  & ROCC & 0.286 & **0.426** & **0.295** & 0.294 & 0.387 & 0.207 & **0.396** \\  & RMSE & 1.126 & 1.185 & 0.850 & 0.718 & 1.110 & 1.134 & 1.837 \\ \hline \multirow{4}{*}{RoMa} & RT & 4.411 s & 4.399 s & 2.715 s & 4.276 s & 3.639 s & 3.507 s & 4.135 s \\  & MI & 0.0286 & 0.0256 & 0.0261 & 0.0276 & 0.0270 & 0.0490 & 0.0350 \\  & ROCC & 0.089 & 0.035 & 0.078 & 0.079 & 0.060 & 0.030 & 0.088 \\  & RMSE & 0.772 & 0.174 & 1.644 & 1.911 & 0.441 & **0.137** & 0.831 \\ \hline \multirow{4}{*}{ours} & RT & **2.025 s** & **1.892 s** & **1.442 s** & **1.640 s** & **1.325 s** & **1.530 s** & **1.363 s** \\  & MI & **0.1030** & **0.0503** & **0.0571** & **0.0498** & **0.0618** & **0.0912** & **0.0720** \\ \cline{1-1}  & ROCC & **0.487** & 0.244 & 0.222 & **0.372** & **0.515** & **0.278** & 0.392 \\ \cline{1-1}  & RMSE & 0.043 & **0.024** & **0.032** & **0.156** & **0.080** & **0.200** & **0.089** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of registration. into a classical optimal transport problem. In the final stage, we applied the RANSAC algorithm to the matched pairs to obtain the transformation model. We evaluated our registration framework extensively, demonstrating that our method surpasses some state-of-the-art models among multiple performance metrics. Quantitative average improvements include up to 20% lower RMSE, 10% higher ROCC, and more than 15% faster runtime. Our method achieves superior performance across multiple metrics, demonstrating the effectiveness of the proposed historical map registration framework. It has potential applications in archaeological research, historical building reconstruction, and urban change tracking analysis. We believe that there are two potential development directions for subsequent registration architectures. Currently, our map registration dataset consists primarily of historical maps based on paper, which are relatively homogeneous.