Research on the state of areas in Ukraine affected by military actions based on remote sensing data and deep learning architectures

The invasion of Ukraine by the Russian Federation and the escalation of military actions in the regions have led to significant damage to residential buildings, civilian infrastructure, various critical infrastructure objects, dams, and extensive pollution of the territories. In this context, the tasks of remote sensing using satellite imagery and aerial observation arise to analyze the impact and conduct an economic assessment of damage in these areas. This work investigates and employs deep neural network (DNNs) models in computer vision (CV) tasks (classification, segmentation) and combines their derivatives, such as convolutional networks (CNNs) and vision transformer models (ViTs), to enhance the accuracy of damage assessment. ViTs have demonstrated significant success, often surpassing traditional CNNs, and have potential applications in remote sensing for damage assessment and the protection of critical infrastructure. The research conducted in this work confirms the importance of applying such technologies in environments where labeled data are rare or non-existent, particularly evaluating the use of DNNs, including CNNs and ViTs, in analyzing regions affected by military actions using synthetic aperture radar (SAR) and multispectral images. The aim and subject of this research also include reviewing the possibilities of combining CNNs and ViTs to improve the speed of image feature extraction, landscape detection, and the detection of complex structural contours of objects, where data are usually insufficient. The results of this study provide a critical review of the application of CNNs and ViTs in remote sensing, identifying significant gaps and challenges, especially in the context of the economic consequences of destruction due to military actions. The technical aspects of using CNNs and transformer-based models for complex CV tasks and transfer learning under data-scarce conditions, as well as the challenges in analyzing large volumes of geophysical data, are considered. The conclusions emphasize the transformational potential of DNNs, especially transformers, in remote sensing under conflict and disaster conditions. Their adaptability and accuracy in various environments underscore their utility in both strategic military and humanitarian contexts, establishing a practical standard for their application in key real, real-world scenario-based territory condition assessment.