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 George Eapen, Naived; George, Jossy Title A name given to the resource CMSFE: Cross-Model SSL Feature Extraction for Enhanced Remote Sensing Data Representation 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;1354 LNNS;pp.527-539 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-4880-1_42" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-4880-1_42</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105019298961?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105019298961?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 George Eapen N., CHRIST (Deemed to be University), Bengaluru, India; George J., CHRIST (Deemed to be University), Bengaluru, India Description An account of the resource Automatic Labeling of Remote Sensing Data fastens analysis in various applications such as environmental monitoring, urban planning, and disaster management. Supervised machine learning approaches rely on labeled datasets created through time-consuming processes. Creation of labeled datasets requires higher resources and such datasets are harder to obtain in most of the domains, and especially in Remote Sensing. This study proposes Cross-Model Self-Supervised Feature Extraction (CMSFE), a novel approach that enhances representation learning in unlabeled remote sensing datasets by integrating features from multiple pre-trained models and refining them through self-supervised learning (SSL). The extracted features are integrated to form a comprehensive and robust feature set that aids in separating different cluster of imagery. Experimental results with EuroSAT dataset demonstrate the quality of feature extraction in separating various classes without any manual intervention or labeling. Dimensionality Reduction and Manifold Learning is applied for visual interpretation of extracted feature space. These features can be further reused for analysis or modeling, highlighting the potential of SSL-based feature extraction methods in remote sensing to enhance representation learning and reduce dependency on labeled data. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Image feature learning; Remote sensing; Representation learning; Satellite imaging; Self supervised learning Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981964879-5; 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