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 Improvized machine learning model for extracting building footprints from collapsed images using high-resolution remote sensing images 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 International Journal of Modeling, Simulation, and Scientific Computing;Issue;;Article No.;2541019; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1142/S1793962325410193" target="_blank" rel="noreferrer noopener">https://doi.org/10.1142/S1793962325410193</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105009641185?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105009641185?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 University, Karnataka, Bangalore, India; Sambandam R.K., Department of Computer Science and Engineering, CHRIST University, Karnataka, Bangalore, India; John S.P., Department of Computer Science and Engineering, Jyothi Engineering College, Cheruthuruthy, Kerala, Thrissur, India Description An account of the resource We propose the development of a robust Enhanced U-Net framework for detecting building objects in images compromised by collapse. Traditional approaches often struggle to identify smaller buildings obstructed by taller structures, trees, or cloud coverage. However, recent advancements in machine learning algorithms present promising opportunities to address these challenges and improve the accuracy of building object detection and damage assessment. The proposed method employs the Siamese U-Net framework, enhanced with novel machine learning algorithms to overcome limitations in existing methodologies and increase the accuracy and reliability of damage assessment, even in complex scenarios. By using augmented satellite images during testing and lowering the building threshold value, our model can accurately predict damaged buildings and retrieve the footprints of smaller structures. The results of this research will advance image analysis techniques, especially in scenarios where collapsed structures pose significant identification and damage assessment challenges. This will be invaluable for government disaster management agencies, insurance companies, and other related organizations. 2025 World Scientific Publishing Company. Subject The topic of the resource augmentation; building footprints; convolutional neural networks; disaster management; image processing; machine learning; Remote sensing; satellite images Publisher An entity responsible for making the resource available World Scientific Relation A related resource ISSN: 17939623; 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