{"version":"1.0","provider_name":"COMPUTAEX Foundation","provider_url":"https:\/\/computaex.es\/en","author_name":"COMPUTAEX","author_url":"https:\/\/computaex.es\/en\/author\/admincenits\/","title":"Federated learning meets remote sensing - Fundaci\u00f3n COMPUTAEX","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"9VGWZIJs8P\"><a href=\"https:\/\/computaex.es\/en\/publicaciones\/federated-learning-meets-remote-sensing\/\">Federated learning meets remote sensing<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/computaex.es\/en\/publicaciones\/federated-learning-meets-remote-sensing\/embed\/#?secret=9VGWZIJs8P\" width=\"600\" height=\"338\" title=\"&#8220;Federated learning meets remote sensing&#8221; &#8212; Fundaci\u00f3n COMPUTAEX\" data-secret=\"9VGWZIJs8P\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script>\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=https:\/\/computaex.es\/wp-includes\/js\/wp-embed.min.js\n<\/script>","description":"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","thumbnail_url":"https:\/\/computaex.es\/wp-content\/uploads\/2025\/08\/COMPUTAEX.jpg","thumbnail_width":1024,"thumbnail_height":403}