Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sajitha, I.; Sambandam, Rakoth Kandan; John, Saju P. Title A name given to the resource Remote sensing data analyzed by machine learning to predict structural changes Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Journal of System Assurance Engineering and Management; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s13198-026-03199-8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s13198-026-03199-8</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034348274?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034348274?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sajitha I., Department of Computer Science and Engineering, Christ (Deemed to be University), Karnataka, Bangalore, India; Sambandam R.K., Department of Computer Science and Engineering, Christ (Deemed to be University), Karnataka, Bangalore, India; John S.P., Department of Computer Science and Engineering, Jyothi Engineering College, Kerala, Cheruthuruthy, Thrissur, India Description An account of the resource Natural disasters can cause extensive structural damage, necessitating rapid and reliable post-event assessment to support emergency response and recovery planning. Although several methods exist for pixel-level damage classification using post-disaster imagery, translating these outputs into meaningful, building-wise assessments remains challenging. Building-level damage prediction provides more interpretable insights, enabling a clearer estimation of the severity of impact on individual structures and a comprehensive understanding of the overall destruction. This information is crucial for quantifying damage magnitude and prioritizing relief operations. This paper proposes Damage Estimation U-Net (DE-U-Net), a deep learning framework designed to estimate structural damage across four classes: No Damage, Minor Damage, Major Damage, and Destroyed. The model is trained on the xBD dataset to learn representative damage patterns. DE-U-Net is developed by integrating a modified Siamese U-Net with a Damage Ratio Analyzer (DRA) algorithm for building-level damage conversion. The DRA algorithm comprises three components: (1) Connected Component Analysis (CCA) to transform pixel-level predictions into building-level predictions (2) size filtering to remove noise and eliminate small artifacts, and (3) a damage estimation module to compute the number of pixels corresponding to each damage class per building. Model performance is evaluated using standard metrics, including accuracy, precision, recall, and F1-score. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2026. Subject The topic of the resource Building damage assessment; Convolutional neural networks; Image processing; Machine learning; Remote sensing; Satellite images; Supervised model Publisher An entity responsible for making the resource available Springer Relation A related resource ISSN: 9756809; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online