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 Fernandes, Melsa Magdalene; Mary Anita, E.A.; Lapina, Maria; Lapin, Vitalii Title A name given to the resource AI-Powered Disaster Management System Using Satellite Imagery: A Survey 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 Lecture Notes in Networks and Systems;Volume;1585 LNNS;pp.161-167 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-01831-1_15" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-01831-1_15</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020726887?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020726887?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 Fernandes M.M., Department of CSE, Christ University, Bangalore Kengeri Campus Kanmanike, Kumbalgodu, Mysore Road, Karnataka, Bangalore, 560074, India; Mary Anita E.A., Department of CSE, Christ University, Bangalore Kengeri Campus Kanmanike, Kumbalgodu, Mysore Road, Karnataka, Bangalore, 560074, India; Lapina M., Department of Computational Mathematics and Cybernetics, North Caucasus Federal University, Pushkina Street, 1, Stavropol, 355017, Russian Federation; Lapin V., Department of Computational Mathematics and Cybernetics, North Caucasus Federal University, Pushkina Street, 1, Stavropol, 355017, Russian Federation Description An account of the resource Disaster management is all about time; timely response and an accurate assessment are the basis on which disaster damage may be limited and lives saved. Traditional methods of disaster response rely on human analysis and manual interpretation of satellite images, which are slow and prone to human error. Here, AI can prove to be a technology capable of using ML and DL algorithms to analyze vast quantities of satellite imagery in real time. AI-based systems can work to detect areas affected, assess the severity of the damage, and predict the evolution of disasters for better response and resource allocation. The paper presents recent developments in AI-based disaster management with the assistance of satellite imagery, sketching out major challenges and future research directions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. Subject The topic of the resource AI; Damage Assessment; Disaster Management; Machine Learning; Satellite Imagery Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303201830-4; 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