Dataset Open Access

Ouagadougou very-high resolution land cover map

Tais Grippa; Stefanos Georganos


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    <subfield code="a">&lt;p&gt;This land cover map of Ouagadougou (Burkina Faso) was created from a WorldView3 very-high resolution imagery with a spatial resolution of 0.5 meter. The methodology followed a open-source semi-automated framework [1] that rely on &lt;a href="https://grass.osgeo.org/"&gt;GRASS GIS&lt;/a&gt;&amp;nbsp;using a local unsupervised optimization approach for the segmentation part&amp;nbsp;[2-3].&lt;/p&gt;

&lt;p&gt;Description of the files:&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;&amp;quot;Landcover.zip&amp;quot; :&amp;nbsp;The direct output from the supervised classification using the Random Forest classifier.&lt;/li&gt;
	&lt;li&gt;&amp;quot;Landcover_Postclassif_Level5_Splitbuildings.zip&amp;quot; : Post-processed version of the previous map (&amp;quot;Landcover&amp;quot;), with reduced misclassifications from the original classification (rule-based used to reclassify&amp;nbsp;the errors, with a focus on built-up classes).&lt;/li&gt;
	&lt;li&gt;&amp;quot;Landcover_Postclassif_Level5_modalfilter3.zip&amp;quot; : Smoothed version of the previous product (modal filter with window 3x3 applied on the &amp;quot;Landcover_Postclassif_Level5_Splitbuildings&amp;quot;).&amp;nbsp;&lt;/li&gt;
	&lt;li&gt;&amp;quot;Landcover_Postclassif_Level6_Shadowsback.zip&amp;quot; : Corresponds to the &amp;quot;level5_Splitbuildings&amp;quot; with shadows coming&amp;nbsp;from the original classification.&lt;/li&gt;
	&lt;li&gt;&amp;quot;Ouaga_legend_colors.txt&amp;quot; : Text file providing the&amp;nbsp;correspondance between the value of the pixels and the legend labels and a proposition of color to be used.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&amp;nbsp;&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; &lt;em&gt;Remote Sensing&lt;/em&gt; 9 (4): 358. &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 &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. &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 &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;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Founding:&amp;nbsp;&lt;/p&gt;

&lt;p&gt;This dataset was&amp;nbsp;produced in the frame of two research project : MAUPP (&lt;a href="http://maupp.ulb.ac.be"&gt;http://maupp.ulb.ac.be&lt;/a&gt;)&amp;nbsp;and REACT (&lt;a href="http://react.ulb.be"&gt;http://react.ulb.be&lt;/a&gt;), funded by the&amp;nbsp;Belgian Federal Science Policy Office (&lt;a href="http://eo.belspo.be/About/Stereo3.aspx"&gt;BELSPO&lt;/a&gt;).&lt;/p&gt;</subfield>
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