## 3 Experiments and Analysis
### Ablation Study
#### 3.3.2 LS Module: Large-Kernel Size Effectiveness  
The large kernel size (\(k\)) in our LS module plays a pivotal role in capturing local spatial context for building target detection, serving as a core parameter for measuring \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(m\)**-UDDRes**} & \multicolumn{2}{c}{**LEVIR-CD+**} & \multicolumn{2}{c}{**S2 Looking**} \\ \cline{2-5}  & **F1** & **IoU** & **F1** & **IoU** \\ \hline
1 & **67.28** & **50.69** & **87.36** & **77.55** \\
2 & 66.65 & 49.98 & 86.79 & 76.66 \\
3 & **66.81** & **50.16** & **87.11** & **77.17** \\
4 & 66.70 & 50.04 & 86.50 & 76.21 \\
5 & **66.87** & **50.23** & **87.16** & **77.25** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Impact of UDDRes repetition (\(m\)) on LS module performance. Color convention: best, 2 nd-best, and **3 rd-best**. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**LEVIR-CD+**} & \multicolumn{3}{c}{**Full Dataset**} & \multicolumn{3}{c}{**Large-VS Subset**} & \multicolumn{3}{c}{**Small-VS Subset**} \\ \hline
**Method** & **Pre.** & **Rec.** & **F1** & **IoU** & **Pre.** & **Rec.** & **F1** & **IoU** & **Pre.** & **Rec.** & **F1** & **IoU** \\ \hline UNet & 73.23 & 75.25 & 74.22 & 59.01 & 81.43 & 73.20 & 77.09 & 62.73 & 66.41 & 72.38 & 69.27 & 52.98 \\ FC-Diff & 78.20 & 73.54 & 75.80 & 61.03 & 82.58 & 71.19 & 76.46 & 61.89 & 72.17 & 71.82 & 72.00 & 56.25 \\ FC-Conc & 71.05 & 77.66 & 74.21 & 58.99 & 80.96 & 75.19 & 77.97 & 63.89 & 60.14 & 77.04 & 67.55 & 51.00 \\ IFNet & 85.83 & 82.56 & 84.16 & 72.66 & 90.85 & 85.09 & 87.87 & 78.37 & 81.28 & 77.55 & 79.37 & 65.80 \\ SNUNet & 85.32 & 80.18 & 82.67 & 70.46 & 88.35 & 81.41 & 84.74 & 73.52 & 83.35 & 76.17 & 79.60 & 66.11 \\ L-UNet & 83.56 & 79.55 & 81.51 & 68.79 & 86.92 & 79.11 & 82.83 & 70.69 & 80.34 & 77.10 & 78.69 & 64.86 \\ BIT & 84.48 & 83.22 & 83.84 & 72.18 & 89.50 & 85.02 & 87.20 & 77.30 & 80.19 & 78.96 & 79.57 & 66.08 \\ ICIF-Net & 85.50 & 81.25 & 83.32 & 71.40 & 90.55 & 82.51 & 86.35 & 75.97 & 79.88 & 77.60 & 78.72 & 64.91 \\ P2V & 78.00 & 80.94 & 79.45 & 65.90 & 84.34 & 81.91 & 83.11 & 71.10 & 70.80 & 77.24 & 73.88 & 85.58 \\ CDNeXt-B & 86.64 & 83.99 & 85.29 & 74.35 & 89.32 & 85.57 & 87.40 & 77.63 & 84.95 & 80.64 & 82.74 & 70.56 \\ CDNeXt & **89.68** & 84.73 & **87.14** & **77.21** & **92.73** & 86.36 & 89.43 & 80.88 & **87.01** & **81.47** & **84.15** & 72.63 \\ \hline RCCD-B & **88.52** & **84.47** & **86.45** & **76.13** & **91.59** & **85.84** & **88.62** & **79.57** & **85.63** & **82.65** & **84.11** & **72.58** \\ RCCD & **89.11** & **86.30** & **87.68** & **78.06** & **91.92** & **89.21** & **90.55** & **82.73** & **86.02** & **82.95** & **84.46** & **73.10** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative performance of RCCD and competing methods on the LEVIR-CD+. Color convention: best, 2 nd-best, and **3 rd-best**. the similarity of local spatial contexts (Figure 7a,b). As shown in Table 5, our experiments indicate that the optimal kernel sizes vary by dataset: \(k=15\) yields the highest F1-score of 67.13% for S2 Looking, while \(k=11\) achieves a peak F1-score of 87.50% for LEVIR-CD+. These optimal values align closely with the datasets' ground resolutions--S2 Looking varies between 0.5 and 0.8 m/pixel (averaging around 0.65 m/pixel), LEVIR-CD+ has a consistent resolution of 0.5 m/pixel. The ratio of ground resolutions (0.65/0.5 = 1.3) closely matches the ratio of optimal kernel sizes (\(15/11\approx 1.36\)), suggesting that the ideal kernel size for measuring building target similarity in local spatial contexts is approximately 23 times the ground resolution (in meters). Note that if the targets of interest include other objects (e.g., vehicles, roads, or trees), the optimal kernel size may need re-evaluation. While larger kernels up to these optimal values improve performance, they also incur a modest increase in computational cost--parameters increase from 49.61 M to 50.58 M and FLOPs from 17.46 G to 17.96 G when \(k\) increases from 1 to 15.