{"id":12586,"date":"2026-09-28T14:46:14","date_gmt":"2026-09-28T12:46:14","guid":{"rendered":"https:\/\/computaex.es\/publicaciones\/federated-learning-meets-remote-sensing\/"},"modified":"2026-09-28T14:46:14","modified_gmt":"2026-09-28T12:46:14","slug":"federated-learning-meets-remote-sensing","status":"publish","type":"page","link":"https:\/\/computaex.es\/en\/publicaciones\/federated-learning-meets-remote-sensing\/","title":{"rendered":"Federated learning meets remote sensing"},"content":{"rendered":"<p><strong>Abstract:<\/strong><\/p>\n<p>Remote sensing (RS) imagery provides invaluable insights into characterizing the Earth\u2019s land surface within the scope of Earth observation (EO). Technological advances in capture instrumentation, coupled with the rise in the number of EO missions aimed at data acquisition, have significantly increased the volume of accessible RS data. This abundance of information has alleviated the challenge of insufficient training samples, a common issue in the application of machine learning (ML) techniques. In this context, crowd-sourced data play a crucial role in gathering diverse information from multiple sources, resulting in heterogeneous datasets that enable applications to harness a more comprehensive spatial coverage of the surface. However, the sensitive nature of RS data requires ensuring the privacy of the complete collection. Consequently, federated learning (FL) emerges as a privacy-preserving solution, allowing collaborators to combine such information from decentralized private data collections to build efficient global models. This paper explores the convergence between the FL and RS domains, specifically in developing data classifiers. To this aim, an extensive set of experiments is conducted to analyze the properties and performance of novel FL methodologies. The main emphasis is on evaluating the influence of such heterogeneous and disjoint data among collaborating clients. Moreover, scalability is evaluated for a growing number of clients, and resilience is assessed against Byzantine attacks. Finally, the work concludes with future directions and serves as the opening of a new research avenue for developing efficient RS applications under the FL paradigm. The source code is publicly available at https:\/\/github.com\/hpc-unex\/FLmeetsRS.<\/p>\n<p><strong>Autores:<\/strong> Sergio Moreno\u2010\u00c1lvarez, Mercedes Eugenia Paoletti, Andres J. Sanchez\u2010Fernandez, Juan\u2010Antonio Rico\u2010Gallego, Lirong Han, Juan Mario Haut<\/p>\n<p><strong>Publicaci\u00f3n \/ Evento:<\/strong> Expert Systems with Applications<\/p>\n<p><a href=\"https:\/\/doi.org\/10.1016\/j.eswa.2024.124583\" target=\"_blank\" rel=\"noopener\">https:\/\/doi.org\/10.1016\/j.eswa.2024.124583<\/a><\/p>","protected":false},"excerpt":{"rendered":"<p>Remote sensing (RS) imagery provides invaluable insights into characterizing the Earth\u2019s land surface within the scope of Earth observation (EO). Technological advances in capture instrumentation, coupled with the rise in the number of EO missions aimed at data acquisition, have significantly increased the volume of accessible RS data. This abundance of information has alleviated the challenge of insufficient training samples, a common issue in the application of machine learning (ML) techniques. In this context, crowd-sourced data play a crucial role\u2026<\/p>","protected":false},"author":1,"featured_media":0,"parent":1583,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"categories":[115,91],"tags":[],"class_list":["post-12586","page","type-page","status-publish","hentry","category-2024-publicaciones","category-publicaciones"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Federated learning meets remote sensing - Fundaci\u00f3n COMPUTAEX<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/computaex.es\/en\/publicaciones\/federated-learning-meets-remote-sensing\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Federated learning meets remote sensing - Fundaci\u00f3n COMPUTAEX\" \/>\n<meta property=\"og:description\" content=\"Remote sensing (RS) imagery provides invaluable insights into characterizing the Earth\u2019s land surface within the scope of Earth observation (EO). 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