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 Srinivasiah, Raghavendra; Jankatti, Santosh Kumar; Lamani, Manjunath Ramanna; Jinachandra, Niranjana Shravanabelagola Title A name given to the resource Change detection and classification of satellite images using convolutional neural network 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 IAES International Journal of Artificial Intelligence;Volume;15;Issue;1;pp.329-337 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.11591/ijai.v15.i1.pp329-337" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijai.v15.i1.pp329-337</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105031521090?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105031521090?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 Srinivasiah R., Machine Learning and Data Science, School of Engineering and Technology, CHRIST University, Bangalore, India; Jankatti S.K., Department of Computer Science and Technology, School of Engineering, Dayananda Sagar University, Bangalore, India; Lamani M.R., Department of Computer Science and Engineering, Moodlakatte Institute of Technology, Kundapura, India; Jinachandra N.S., Department of Mechanical Engineering, School of Engineering and Technology, CHRIST University, Bangalore, India Description An account of the resource Satellite and airborne imagery, collectively known as earth observation imagery, are images of the earth collected from spaceborne or airborne platforms such as satellites and aircraft. Over the last 100 years, with the fast development of aviation, space exploration, and imaging technologies, the coming together of these technologies has been inevitable. Earth observation imagery has many applications in regional planning, geology, reconnaissance, fishing, meteorology, oceanography, agriculture, biodiversity conservation, forestry, landscape, intelligence, cartography, education, and warfare. With the rise in the number of these airborne and spaceborne imaging platforms being deployed by government and private entities alike, the capability to sift through and analyze vast amounts of data generated by these platforms is the need of the hour. With the exponential improvement in the computational capabilities of computers over the last half a century, analysts are exceedingly moving towards the practice of artificial intelligence, machine learning (ML), and computer vision solutions to automate a large part of the processes employed in analyzing earth observation imagery. This work recommends a workflow to perceive and classify changes in earth observation imagery of a given area by utilizing the vast flexibility that convolutional neural networks (CNN) provide. This is an open access article under the CC BY-SA license. https://creativecommons.org/licenses/by-sa/4.0/. Subject The topic of the resource Artificial intelligence; Computer vision; Convolutional neural network; Machine learning; Satellite image Publisher An entity responsible for making the resource available Institute of Advanced Engineering and Science Relation A related resource ISSN: 20894872; 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 All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online