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This paper presents the development of a Geographic Information System (GIS) designed to monitor and analyze the destruction caused by the ongoing war in Ukraine, with a focus on addressing the limitations of existing systems. The research integrates multimodal data and provides a scalable, evolving solution for documenting damage using advanced technologies, including satellite imagery, temporal maps, and 3D visualizations. The methodology combines modern web technologies (Node.js, Nuxt 3, PostgreSQL) with a thin client-server architecture for centralized data processing and Docker for flexible deployment. Advanced algorithms enable real-time analysis and visualization of destruction patterns. Key findings demonstrate the system’s ability to efficiently manage large datasets (exceeding X GB) with response times of under Y seconds, maintaining reliable performance under load. This GIS offers a significant advancement in documenting military conflicts and is adaptable to broader disaster monitoring applications. Future developments include enhanced AI capabilities for automated multimodal data processing and dynamic tracking of destruction over time, further supporting emergency response and humanitarian monitoring.

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Scalable Geoinformation System for Damage Dynamics in Conflict Zones: Ukraine Case Study

  • 会议论文

  • 发表日期：

    2026年

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  • Bohdan Stetsenko,
  • Vladyslav Taran,
  • Yuri Gordienko,
  • Sergii Stirenko

摘要

This paper presents the development of a Geographic Information System (GIS) designed to monitor and analyze the destruction caused by the ongoing war in Ukraine, with a focus on addressing the limitations of existing systems. The research integrates multimodal data and provides a scalable, evolving solution for documenting damage using advanced technologies, including satellite imagery, temporal maps, and 3D visualizations. The methodology combines modern web technologies (Node.js, Nuxt 3, PostgreSQL) with a thin client-server architecture for centralized data processing and Docker for flexible deployment. Advanced algorithms enable real-time analysis and visualization of destruction patterns. Key findings demonstrate the system’s ability to efficiently manage large datasets (exceeding X GB) with response times of under Y seconds, maintaining reliable performance under load. This GIS offers a significant advancement in documenting military conflicts and is adaptable to broader disaster monitoring applications. Future developments include enhanced AI capabilities for automated multimodal data processing and dynamic tracking of destruction over time, further supporting emergency response and humanitarian monitoring.