# Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images
## 4 Conclusions  
The change detection of buildings is a challenge because it is easily interfered with by the shadows generated by light and the similar color features around the buildings, resulting the problems such as dense distribution and susceptibility to occlusion. To address these problems, this paper proposes a building change detection model based on edge guidance to accurately extract building change information, namely EGHNet. The model includes a hierarchical encoder-decoder network, an edge-extracted module, and an edge-guided module. The edge-extracted module extracts the shallow and deep features in the bi-temporal image. The edge-extracted module combines the shallow and deep features to extract the edge information of the target object so as to effectively obtain the accurate edge map of the changed feature. The edge-guided module fuses the edge information with the difference information, increases the weight of the target edge, and guides the neural network to focus on the easily confused building edge region so as to improve the  
Figure 9: Case on the post-disaster reconstruction of Christchurch in New Zealand. (**a**) T1 image. (**b**) T2 image. (**c**) Ground truth. (**d**) Change maps generated by EGHNet. In the color classification, the true positive is white, the true negative is black, the false positive is red, and the false negative is green. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **GFLOPs (G)** & **Para (M)** & **F1 (\%)** \\ \hline FC-EF\({}_{18}\) & 15.93 & 5.48 & 73.14 \\ FC-Siam-Conc\({}_{15}\) & 21.71 & 5.47 & 80.52 \\ FCN-PP\({}_{19}\) & 45.79 & 13.39 & 86.54 \\ Unet++MSOF\({}_{19}\) & 45.43 & 11 & 87.88 \\ DSFN\({}_{20}\) & 82.26 & 50.44 & 79.21 \\ BIT-CD\({}_{22}\) & 8.75 & 3.5 & 89.35 \\ ChangFormer\({}_{23}\) & 202.7 & 41.02 & 88.03 \\ EGHNet & 112.09 & 25.06 & 91.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Efficiency Analysis on the WHU Dataset. network's change detection performance. We compare the proposed change detection model with existing models on two publicly available building change detection datasets. Experimental results demonstrate the superiority of the proposed model. In future work, several directions can be pursued to further enhance the applicability of the proposed model. Extending the approach to detect other types of changes beyond buildings, such as natural landscape alterations or infrastructure developments, could broaden its utility. Integrating additional data sources, such as other satellite bands or sensor data, might provide more comprehensive insights and improve detection accuracy. Additionally, exploring transfer learning techniques could enable the model to be adapted to new datasets or geographic regions with minimal retraining, enhancing its generalizability and applicability to a wider range of change detection tasks. In real-world scenarios, our edge-focused methodology has potential cross-disciplinary applications, including humanitarian remote sensing, particularly in the detection of landtimes and unexploded ordance. Our approach is exceptionally adept at addressing the challenges posed by remotely triggered and non-metallic scatterable landmines. These landmines, which typically contain minimal metallic components, present significant detection challenges in complex environments. The enhanced edge detection capabilities of our proposed model could improve the identification and localization of these hazardous objects, facilitating safer and more efficient surveying of affected areas. Conceptualization, M.Y. and S.H. ; methodology, Y.Z. ; software, Y.F. ; validation, M.Y., Y.Z. and S.H. ; formal analysis, Y.F. ; investigation, Y.Z. ; resources, Y.Z. ; data curation, Y.F. ; writing--original draft preparation, M.Y. ; writing--review and editing, Y.Z. and S.H. ; visualization, Y.F. ; supervision, Y.Z. ; project administration, Y.Z. ; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.