March 16, 2020 Journal article Open Access

Stefanos Georganos; Tais Grippa

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        <foaf:name>Stefanos Georganos</foaf:name>
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        <foaf:name>Tais Grippa</foaf:name>
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    <dct:title>Kampala Very-High-Resolution Land Cover Map</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2020</dct:issued>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2020-03-16</dct:issued>
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    <dct:description>&lt;p&gt;This is a very-high-resolution map of Kampala derived from satellite imagery of Pleiades (0.5m) collected in February 2013.&lt;/p&gt; &lt;p&gt;The pixel values related to the following legend:&lt;/p&gt; &lt;p&gt;2: Water&lt;/p&gt; &lt;p&gt;3: Tree vegetation&lt;/p&gt; &lt;p&gt;4:Low vegetation&lt;/p&gt; &lt;p&gt;5:Bare ground&lt;/p&gt; &lt;p&gt;6: Artificial ground surface&lt;/p&gt; &lt;p&gt;7:Buildin&lt;/p&gt; &lt;p&gt;8:Shadow&lt;/p&gt; &lt;p&gt;The Out of Bag error of the product is 14,14%. The class errors are:&lt;/p&gt; &lt;p&gt;Water =&amp;nbsp;0.077748&lt;/p&gt; &lt;p&gt;Tall Vegetation =&amp;nbsp;0.410714&lt;/p&gt; &lt;p&gt;Low Vegetation =&amp;nbsp;0.087757&lt;/p&gt; &lt;p&gt;Bare Ground =&amp;nbsp;0.336689&lt;/p&gt; &lt;p&gt;Artificial Ground Surface =&amp;nbsp;0.197932&lt;/p&gt; &lt;p&gt;Building =&amp;nbsp;0.058271&lt;/p&gt; &lt;p&gt;Shadow =&amp;nbsp;0.062032&lt;/p&gt; &lt;p&gt;References:&lt;/p&gt; &lt;p&gt;[1]&amp;nbsp;Grippa, Ta&amp;iuml;s, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and El&amp;eacute;onore Wolff. 2017. &amp;ldquo;An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.&amp;rdquo;&amp;nbsp;&lt;em&gt;Remote Sensing&lt;/em&gt;&amp;nbsp;9 (4): 358.&amp;nbsp;&lt;a href="https://doi.org/10.3390/rs9040358"&gt;https://doi.org/10.3390/rs9040358&lt;/a&gt;.&lt;/p&gt; &lt;p&gt;[2]&amp;nbsp;Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and El&amp;eacute;onore Wolff. 2017. &amp;ldquo;A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.&amp;rdquo; In&amp;nbsp;&lt;em&gt;Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II.&lt;/em&gt;, edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE.&amp;nbsp;&lt;a href="https://doi.org/10.1117/12.2278422"&gt;https://doi.org/10.1117/12.2278422&lt;/a&gt;.&lt;/p&gt; &lt;p&gt;[3]&amp;nbsp;Georganos, Stefanos, Ta&amp;iuml;s Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. &amp;ldquo;SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.&amp;rdquo; In&amp;nbsp;&lt;em&gt;Proceedings of the 2017 Conference on Big Data from Space (BiDS&amp;rsquo;17)&lt;/em&gt;.&lt;/p&gt; &lt;p&gt;This research was funded by BELSPO (Belgian Federal Science Policy Office) in the frame of the STEREO III program, as part of the REACT (SR/00/337) project (&lt;a href="http://react.ulb.be/"&gt;http://react.ulb.be/&lt;/a&gt;).&lt;/p&gt;</dct:description>
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