## 4 Results and Analysis
### Change Detection of Destroyed Buildings  
The calculated DPNCI maps of the time series are shown in Figure 10. Pixel brightness represents the stability of the building area in Mariupol. With the occurrence and persistence of conflicts, white pixels gradually decrease, indicating that the stability of urban building areas is disturbed.  
The frequency distribution of the DPNCI in the artificial building land is shown in Figure 11. Its distribution gradually changes from the right skew (before the conflict) to the left skew (after the conflict), and the average coherence keeps decreasing. Especially between 12 March and 24 March, the artificial building showed significant decoherence.  
Figure 11: The frequency distribution of the DPNCI in the artificial building land.  
Figure 10: Time series DPNCI maps of the study area.  
To verify the sensitivity of the DPNCI to the detection of damaged buildings, we superimposed the urban burning area extracted in Section 3.1 with the OSM building vectors, screened out the pixels of buildings located in the burning area, and calculated the changes of their DPNCI values. It is reasonable to assume that, in modern armed conflicts, a weapon capable of causing a building to explode and burn would also do great damage to the structure of the building. Therefore, it is reasonable to consider the buildings in the burning area as damaged buildings.  
In Figure 12, the different colored line segments represent the average DPNCI value changes of the burned buildings over three different time periods. The blue line showed the largest decrease in coherence between 12 March and 24 March and remained at a low level, while the green and black lines also showed the largest decrease in coherence between 24 March and 5 April, 5 April and 17 April, respectively. It is proved that this method can accurately detect the time node of damaged buildings.  
In order to quantitatively describe the damaged degree and spatial and temporal distribution of buildings in Mariupol, we graded the damage degree and geographically located 543 buildings in the collected images and photos. Four damaged levels were divided according to visual interpretation, as shown in Table 2. 80% of the buildings were selected to determine the classification threshold, and the remaining 20% were used as verification data. The confusion matrix of inspection results is shown in Figure 13, among which the classification accuracy of destroyed buildings is the highest, reaching 69%, and the overall accuracy is 59.75%.  
Figure 12: DPNCI value changes of burned buildings.  
As shown in Figure 14, buildings in Mariupol have been severely damaged in general, among which 79.2% are severely damaged or destroyed, mainly in Azovstal steel and iron works and the surrounding central city in the south, Ilyich Iron and Steel Works and municipal center in the north, and around the port area in the southwest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Damaged Degree** & **Description** & **Example** & **DPNCI** \\ \hline Possible damaged or & The structure of the building & & \\ Possible damaged or & is intact, and no obvious & & \\  undamaged & cracks or structural changes & & \\  undamaged & can be identified from the & & \\ & The structure of the building & & \\ & is relatively intact, with & & \\ Moderate damage & partially damaged to the top & & \\ & or sides and no apparent & & \\ & collapse. & & \\ & The roof and facade of the & & \\ Severe damage & building were badly hit, with & & \\ & extensive collapse, but parts of & & \\ & the wall structure remained. & & \\ Destroyed & The building was completely & & \\ & destroyed in a pile of rubble, & & \\ & making it difficult to see intact & & \\ & parts of the walls. & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Damaged buildings level.  
Figure 13: Confusion matrix for accuracy verification.