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license.html
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<!DOCTYPE HTML>
<!--
Theory by TEMPLATED
templated.co @templatedco
Released for free under the Creative Commons Attribution 3.0 license (templated.co/license)
-->
<html>
<head>
<title>License information</title>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<link rel="stylesheet" href="assets/css/main.css" />
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<h1>Deep Learning for Remote Sensing Scene Classification</h1>
<h3>This work aims to explore how to boost the performance of small-scale convolutional neural networks</h3> <!--Learning from spatial data: unveiling the geo-environment through quantitative approaches </h2>
-->
</section>
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<h2>License</h2>
<p>Creative Commons Attribution 4.0</p>
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<h2>Content: Deep Learning for Remote Sensing Scene Classification </h2>
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<p>This work is open source and shared under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0</a>. For citation, please refer to the EGU abstract.</p>
<h2>The HTML template</h2>
<p>The template used to generate this website was downloaded from <a href="https://templated.co">Templated.co</a>. The images can be found at <a href="https://unsplash.com">Unsplash</a>, or on the website of the University of Lausanne,
and the credits for the images are <a href="CREDITS.txt">here</a>. </p>
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© UnilNet | HTML template: <a href="https://templated.co">Templated.co</a> | Images: <a href="https://unsplash.com">Unsplash</a>.
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