Oliinyk, YuriiRumiantsev, Oleksii2026-02-062026-02-062025Oliinyk, Y. Evaluation of the effectiveness of two approaches to building damage detection with satellite imagery / Oleksii Rumiantsev, Yurii Oliinyk // Information, Computing and Intelligent systems. – 2025. – No. 7. – P. 61-71. – Bibliogr.: 15 ref.https://ela.kpi.ua/handle/123456789/78686This study addresses the approaches for satellite image analysis to assess infrastructure damage. Themain aim is to conduct a comprehensive comparative analysis of the effectiveness of two key machinelearning approaches: specialized semantic segmentation based on theU-Netarchitecture and generalizedvisual analysis using large vision-language models. The object of the research is the process of quantitativelybenchmarking these two distinct approaches to determine their practical applicability for multi-class damageclassification.The research material is the publicly availablexView2dataset. The methods involved two parallelexperiments. For the semantic segmentation approach, aU-Netmodel with anEfficientNet-B4encoderwas implemented and trained on 6-channel input data (”before” and ”after” images) using a combinedDiceandFocalloss function. For the vision-language models approach, the open-sourceLLaVA-1.5-7Bmodelwas evaluated in a zero-shot mode using advanced prompt engineering for an aggregative counting task.To enable a direct comparison, the standardJaccard indexwas calculated based on the aggregated objectcounts for each damage class.The results of the experiments revealed a significant performance disparity. The specializedU-Netmodeldemonstrated high effectiveness, achieving an intersection over union score of 0.6141 on the test set. Incontrast, theLLaVAmodel proved unsuitable for accurate quantitative analysis, yielding an extremely lowJaccard indexof approximately 0.063, primarily due to its systemic failure to correctly identify and countobjects (𝑅𝑒𝑐𝑎𝑙𝑙≈0.07). The scientific novelty lies in being the first study to quantitatively document thisorder-of-magnitude capability gap, confirming that for tasks requiring high-precision mapping, specializedsegmentation models remain the indispensable tool.ensatellite image analysisdamage detectionsemantic segmentationU-Netlarge vision-languagemodelаналіз супутникових знімківоцінка руйнуваньсемантична сегментаціявеликі зорово-мовні моделіArticleP. 61-71https://doi.org/10.20535/2786-8729.7.2025.341475004.930000-0002-7408-49270009-0005-7223-3633