###### Abstract  
Modern armed conflicts can cause serious humanitarian disasters, and remote sensing technology is critical in monitoring war crimes and assessing post-war damage. In this study, a constrained energy minimization algorithm incorporating the feature bands (IFB-CEM) is designed to detect urban burning areas in optical images. Due to the difficulty of obtaining the ground survey data of the battlefield, the dual-polarization normalized coherence index (DPNCI) is designed based on the multi-temporal synthetic aperture radar (SAR) image, and the quantitative inversion and evaluation of the destruction of urban architecture are combined with the public images on the Internet. The results show that the burning area is widely distributed in the armed conflict region, and the distribution is most concentrated around the Azovstal steel and iron works. The burning area reached its peak around 22 March, and its change is consistent with the conflict process in time and space. About 79.2% of the buildings in the city were severely damaged or completely destroyed, and there was a significant correlation with burning exposure. The results of this study show that publicly available medium-resolution remote sensing data and Internet information have the ability to respond quickly to the damage assessment of armed conflict and can provide preliminary reference information for dealing with humanitarian disasters. Russia-Ukraine conflict; urban burning detection; synthetic aperture radar; coherence change detection; damage assessment 2023153096. 12 June 202313 June 20  
aid and post-disaster reconstruction has become an urgent problem for the international community to solve in the face of armed conflict crisis. The development of earth observation technology provides a better choice to solve this problem. Remote sensing images can quickly obtain a wide range of surface information and effectively detect the occurrence of malignant events and ground object damage in humanitarian disaster areas. Therefore, some research institutions or researchers, including the United Nations Institute for Training and Research (UNITAR), have conducted humanitarian disaster monitoring and damage assessment in Iraq, Yemen, Syria and other countries based on different types of remote sensing images [3; 4; 5; 6]. In high-resolution optical remote sensing images (such as Worldview, Quickbird, GeoEye), individual infrastructure damage and the occurrence of small crowd gatherings can be detected. [PERSON] et al. used Plant, a high-resolution satellite constellation with a spatial resolution of 3 m, to detect the potentially burned villages [7] in the ethnic cleansing campaign of Rohingya villages in northern Rakhine State by the Myanmar military in 2017 [8]. UNITAR evaluated the level of building damage in some areas during the conflict between Russia and Ukraine through visual interpretation based on the Worldview satellite with a resolution of 0.5 m [9]. However, the high cost and small imaging range of high-resolution optical remote sensing images generally limits their application in large-scale armed conflicts. Medium-resolution optical remote sensing images (such as Landsat series, Sentinel-2 series and MODIS) have been widely used in large-scale armed conflict monitoring due to their stable imaging quality, large observation perspective and short revisit period. The main work is reflected in the detection of thermal anomaly areas in conflicts and the detection of regional surface changes. For example, MODIS thermal anomaly detection data products are applied to reveal the potential relationship between fire growth and violent conflicts in Darfur, Sudan, proving that objective information on some violent conflicts may be provided based on the global daily fire detection situation [10]. Landsat ETM+ was used to detect changes in the Darfur region of Sudan, and it was pointed out that the cause of changes might be related to burning by comparing the albedo data of the adjacent two years [11].