# A Registration Method for Historical Maps Based on Self-Supervised Feature Matching
## 4 Results
### MapSegment  
To validate the necessity of image segmentation preprocessing, we conducted a controlled experiment comparing registration performance on raw historical maps versus segmented maps processed by the proposed MapSegment pipeline.  
We applied MapSegment to the images in Figure 5, with results demonstrated in Figure 6. The original images include historical maps with various drawing styles. Some images exhibit noticeable creases (a, b, c), evident signs of paper aging (d, g, h), and the presence of irrelevant text or color interference (e, f). These noise elements negatively impact the registration process. To address this, we applied image segmentation methods for preprocessing. As shown in Figure 6, preprocessing preserves key cartographic structures (e.g., roads, buildings) while suppressing extraneous elements (e.g., chromatic variations, textual annotations). The segmentation results make the main road information in the images significantly clearer, with noise notably reduced. From an observational perspective, the primary features expressed in the images become easier to distinguish.  
Figure 5: Original maps of Beijing City in different historical periods.  
Figure 6: Segment maps of Beijing City in different historical periods.  
The preprocessed maps form the core of our Historical Map Registration Benchmark (HMRB), ensuring domain-consistent evaluation for subsequent experiments. Visual inspection confirms enhanced discriminability of key features, enabling robust correspondence estimation despite stylistic variations.  
To rigorously evaluate the efficacy of MapReduceSegment, we established SuperPoint and SuperGlue as baseline models. Feature matching was performed on both raw historical maps and segmented maps, with comparative results shown in Table 2 and visualized in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Process** & **Metrics** & **a-b** & **a-c** & **a-d** & **a-e** & **a-f** & **a-g** & **a-h** \\ \hline \multirow{4}{*}{-} & RT & 1.104 s & 1.271 s & 1.086 s & 1.080 s & 1.106 s & 1.091 s & 1.089 s \\  & Ref & 3622 & 3622 & 3622 & 3622 & 3622 & 3622 & 3622 \\ \cline{1-1}  & Sen & 3186 & 4001 & 2819 & 2623 & 3626 & 2921 & 2806 \\ \cline{1-1}  & \(N_{all}\) & 1980 & 66 & 30 & 185 & 53 & 1974 & 888 \\ \hline \multirow{4}{*}{MapSegment} & RT & 1.079 s & 1.094 s & 1.094 s & 1.063 s & 1.092 s & 1.062 s & 1.067 s \\ \cline{1-1}  & Ref & 3559 & 3559 & 3559 & 3559 & 3559 & 3559 & 3559 \\ \cline{1-1}  & Sen & 2425 & 4144 & 3876 & 2959 & 4034 & 2893 & 3136 \\ \cline{1-1}  & \(N_{all}\) & **2418** & **501** & **303** & **630** & **252** & **2400** & **1829** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effectiveness of MapReduceSegment.  
Figure 7: Feature matching results on original and segment map.  
Compared with the original image, the effective matching pairs (\(N_{all}\)) obtained by matching the segmented image have an average improvement of 15%. Original maps exhibited sparser correspondences (mostly <15% inlier ratio) with clustered spatial distributions, particularly when stylistic or structural discrepancies existed between pairs. In contrast, segmented maps achieved much higher spatially uniform correspondences.