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<!DOCTYPE HTML>
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<head>
<title>Deep Learning for Remote Sensing Scene Classification</title>
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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>
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<h3>EGU Presentation</h3>
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<p>This work was partly presented at the EGU 2018 in the session <i>Learning from spatial data: unveiling the geo-environment through quantitative approaches.</i></p>
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<a href="https://www.researchgate.net/publication/323316489_Deep_Learning_for_Remote_Sensing_Scene_Classification_A_Simple_and_High-Performance_Architecture" class="button special">More</a>
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<p>Here, you will find some Python codes. A step-by-step implementation using two Python libraries: TenserFlow and Keras with the TensorFlow back-end. </p>
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<p>The benchmark datasets used in this work can be easily downloaded to a local workstation. They are freely available from open-source repositories.</p>
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<h2>Meet the team</h2>
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<h3>Mohamed Laib</h3>
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<li><a href="https://github.com/mlaib"><img src="images/github.png" height="20" width="20"/></a></li>
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<h3>Jean Golay</h3>
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<h3>Fabian Guignard</h3>
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<h3>Mikhail Kanevski</h3>
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<h2>Work place</h2>
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</br></br>
<b>Address</b>
<p><a href="http://wp.unil.ch/geocomputing/"><b>Swiss Geocomputing Centre</b></a> <br>
Quartier UNIL-Mouline, Building Géopolis <br> 1015 Lausanne, Switzerland.</br>
</p>
<b>Acknowledgment</b>
<p>A special thank to <a href="https://www.researchgate.net/profile/Ludovic_Raess"><b>Ludovic Räss</b></a> </br> for his assistance and input regarding</br>
the efficient use of GPU clusters .<br>
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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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