AI-Powered Disaster Management System Using Satellite Imagery: A Survey

Title

AI-Powered Disaster Management System Using Satellite Imagery: A Survey

Creator

Fernandes, Melsa Magdalene; Mary Anita, E.A.; Lapina, Maria; Lapin, Vitalii

Description

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.

Source

Lecture Notes in Networks and Systems;Volume;1585 LNNS;pp.161-167

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

AI; Damage Assessment; Disaster Management; Machine Learning; Satellite Imagery

Coverage

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

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-303201830-4;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-032-01831-1_15

https://www.scopus.com/pages/publications/105020726887?origin=resultslist