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 Conference Paper Faculty Publications- Conference Papers 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 Ikkara, Sajitha; Sambandam, Rakoth Kandan; John, Saju P. Title A name given to the resource Evaluating Building Damage Classification Accuracy: A Benchmarking Study of UNet Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE International Conference on Next-Gen Technologies of Artificial Intelligence and Geoscience Remote Sensing, EarthSense 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/EarthSense66084.2025.11297371" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/EarthSense66084.2025.11297371</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032199046?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032199046?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 Ikkara S., Christ University, Dept. of Computer Science and Engineering, Karnataka, Bangalore, India; Sambandam R.K., Jyothi Engineering College, Dept. of Computer Science and Engineering, Kerala, Thrissur, India; John S.P., Christ University, Dept. of Computer Science and Engineering, Karnataka, Bangalore, India Description An account of the resource Building damage classification must be done accurately and quickly in order to support disaster response and recovery activities. Deep learning models, particularly U-Net, have demonstrated strong potential in automating damage assessment from satellite and aerial imagery. This study benchmarks the accuracy of U-Net in classifying building damage across multiple datasets, evaluating its performance against ground truth labels. Key factors such as data preprocessing, augmentation techniques, and model variations are analyzed to determine their impact on classification accuracy. The results provide insights into the strengths and limitations of variations in U-Net for damage assessment, highlighting areas for improvement and future research directions 2025 IEEE. Subject The topic of the resource augmentation; classification; damage assessment; Deep learning; satellite images Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833150288-1; Language A language of the resource English Type The nature or genre of the resource Conference paper 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