## 6 Conclusions  
In this study, the city of Mariupol, which was severely affected during the conflict between Russia and Ukraine, was taken as the research area. Landsat-8/9, Sentinel-1/2, and open-source community data on the Internet, which were publicly available, were used to monitor the temporal and spatial changes of urban burning areas and damaged buildings. The IFB-CEM designed by us has achieved excellent results in detecting burning areas. Through statistical analysis of the burning area, the intensity of conflict reached its peak around 22 March 2022. The spatial growth of damaged buildings was positively correlated with the course of the conflict. By the time the Russian military took full control of Mariupol (End of May 2022), almost all buildings in the city had been hit, with the cumulative proportion of destroyed and severely damaged buildings being 79.2%.  
Obviously, medium-resolution SAR satellites are not capable of detecting the extent of damage to small buildings, even though they can provide a large-scale view of the distribution and trend of damage. The timing mismatch between social media pictures and remote sensing images is also one of the important reasons that restrict the accuracy of building damage degree inversion. With today's convenient access to massive data, it is  
Figure 15: Survey of damage to buildings in Mariupol by UNITAR.  
difficult to locate the location of social media image events only by visual interpretation, and a more intelligent and automated process is urgently needed to assist this work. In the future, we will build a more streamlined armed conflict monitoring platform, design more sophisticated damaged building indicators and rating standards based on higher resolution SAR images, give full play to the potential of current open source data and public Internet data in assisting conflict damage detection, expand our detection scope, and carry out fine-grained conflict damage assessment.  
Conceptualization, G.J. and Q.H.; methodology, G.J. and Q.H.; software, Q.H. and Y.X.; validation, G.J., XX. and H.Y; formal analysis, H.Y.; investigation, Y.X.; resources, Y.X. and H.Y.; data curation, Q.H.; writing--original draft preparation, Q.H.; writing--review and editing, Q.H., X.X. and G.J.; visualization, Q.H.; supervision, X.X.; project administration, G.J. and Q.H.; funding acquisition, G.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grants 41474010, 61401509 and 42201492.  
Not applicable.  
We would like to express our gratitude to the European Space Agency (ESA) and the United States Geological Survey (USGS), which provided the Sentinel data and Landsat data. We also thank Open Street Map (OSM) and its users for providing vector data to the study area and MAXAR for sharing the high-resolution image data set, as well as others, such as Zenger, who shared the images on Internet platforms. Finally, we would like to express our thanks to the reviewers and editors who have made valuable comments to improve the quality of this paper.  
The authors declare no conflict of interest.