Dar Es Salaam Very-High-Resolution Land Cover Map

<p>This is a very-high-resolution land cover map of Dar es Salaam derived from satellite imagery (Pleiades, 0.5m resolution). The majority of the area is classified from a 2016 (July) image while a small part of it from two images collected in January and March 2018, respectively.</p> <p>&nbsp;The pixel values related to the following legend:</p> <p>5=tree<br> 8=shadow<br> 3=artificial ground surface<br> 4=low vegetation<br> 2=water<br> 7=bare ground<br> 1=building<br> 113=high elevated buildings<br> 112=medium elevated buildings<br> 111=low elevated buildings</p> <p>The Out of Bag error of the product is 6,38% with the following class errors:</p> <p>Building =&nbsp;0.035826</p> <p>Water =&nbsp;0.049934</p> <p>Artificial Ground Surface =&nbsp;0.077108</p> <p>Low Vegetation = 0.108709</p> <p>Tall Vegetation =&nbsp;0.062278</p> <p>Bare Ground =&nbsp;0.13803</p> <p>Shadow =&nbsp;0.019872</p> <p>References:</p> <p>[1]&nbsp;Grippa, Ta&iuml;s, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and El&eacute;onore Wolff. 2017. &ldquo;An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.&rdquo;&nbsp;<em>Remote Sensing</em>&nbsp;9 (4): 358.&nbsp;<a href="https://doi.org/10.3390/rs9040358">https://doi.org/10.3390/rs9040358</a>.</p> <p>[2]&nbsp;Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and El&eacute;onore Wolff. 2017. &ldquo;A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.&rdquo; In&nbsp;<em>Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II.</em>, edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE.&nbsp;<a href="https://doi.org/10.1117/12.2278422">https://doi.org/10.1117/12.2278422</a>.</p> <p>[3]&nbsp;Georganos, Stefanos, Ta&iuml;s Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. &ldquo;SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.&rdquo; In&nbsp;<em>Proceedings of the 2017 Conference on Big Data from Space (BiDS&rsquo;17)</em>.</p> <p>This dataset was&nbsp;produced in the frame of &nbsp;REACT (<a href="http://react.ulb.be/">http://react.ulb.be</a>), funded by the&nbsp;Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p>