Ouagadougou very-high resolution land cover map (Q11790)

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 GRASS GISusing a local unsupervised optimization approach for the segmentation part[2-3]. Description of the files: Landcover.zip :The direct output from the supervised classification using the Random Forest classifier. Landcover_Postclassif_Level5_Splitbuildings.zip : Post-processed version of the previous map (Landcover), with reduced misclassifications from the original classification (rule-based used to reclassifythe errors, with a focus on built-up classes). Landcover_Postclassif_Level5_modalfilter3.zip : Smoothed version of the previous product (modal filter with window 3x3 applied on the Landcover_Postclassif_Level5_Splitbuildings). Landcover_Postclassif_Level6_Shadowsback.zip : Corresponds to the level5_Splitbuildings with shadows comingfrom the original classification. Ouaga_legend_colors.txt : Text file providing thecorrespondance between the value of the pixels and the legend labels and a proposition of color to be used. References: [1]Grippa, Tas, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and Elonore Wolff. 2017. An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification. Remote Sensing 9 (4): 358. https://doi.org/10.3390/rs9040358. [2]Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and Elonore Wolff. 2017. A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery. In Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II., edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE. https://doi.org/10.1117/12.2278422. [3]Georganos, Stefanos, Tas Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas. In Proceedings of the 2017 Conference on Big Data from Space (BiDS17). Founding: This dataset wasproduced in the frame of two research project : MAUPP (http://maupp.ulb.ac.be)and REACT (http://react.ulb.be), funded by theBelgian Federal Science Policy Office (BELSPO).