Research on the effectiveness of neural network detection of plots with the destroyed buildings remains

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dc.contributor.authorDidur, V.
dc.contributor.authorMolchanova, M.
dc.contributor.authorMazurets, O.
dc.contributor.authorМазурець, Олександр Вікторович
dc.date.accessioned2025-05-29T15:55:54Z
dc.date.available2025-05-29T15:55:54Z
dc.date.issued2025
dc.description.abstractThis paper explores the effectiveness of neural network approaches for detecting and classifying plots containing the remains of destroyed buildings using aerial imagery. The proposed method integrates a YOLO-based object detector and a Vision Transformer for multi-class classification of structural debris such as concrete, metal, brick, and wood. The system achieves high accuracy (97%) and demonstrates strong performance across key classification metrics. This research highlights the critical role of deep learning in accelerating post-disaster damage assessment, supporting emergency response, cultural heritage preservation, and long-term urban resilience planning.
dc.identifier.citationDidur V., Molchanova M., Mazurets O. Research on the effectiveness of neural network detection of plots with the destroyed buildings remains. Modern technologies and science: problems, new and relevant developments. Proceedings XXI International Scientific and Practical Conference. May 26, 2025. Zaragoza, Spain. Pp. 245-251
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/18411
dc.language.isoen
dc.titleResearch on the effectiveness of neural network detection of plots with the destroyed buildings remains
dc.typeСтаття
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