Spuspo: Spatially Partitioned Unsupervised Segmentation Parameter Optimization For Efficiently Segmenting Large Heterogeneous Areas

This dataset contains several data, results and processing material from the application of GEOBIA-based, Spatially Partitioned Segmentation Parameter Optimization (SPUSPO) in the city of Ouagadougou. In detail in contains: <strong>A Land Use - Land Cover map of Ouagadougou derived through SPUSPO. The classifier used was Extreme Gradient Boosting (XGBoost). </strong> Labels : 2 : Artificial Ground Surface 0 : Building 5 : Low Vegetation 4 : Tree 1 : Swimming Pool 3 : Bare Ground 7 : Shadow 6 : Inland Water <strong>The training and test data used in the study (SPUSPO and benchmark approach). </strong> The data are given in a csv format. <strong>The Jupyter notebook code which involves Python and GRASS GIS to automatize and efficiently perform SPUSPO in a large dataset.</strong> Python code calling GRASS GIS functions for automatizing the procedure. <strong>The segmentation layers coming from SPUSPO and the benchmark approaches (in raster formats due to data limitations).</strong> Segmentation rasters for each approach. <strong>The R code for optimization of XGBoost as well as feature selection with VSURF and classification of the whole dataset.</strong> <strong>Segmentation evaluation metrics.</strong> A csv file with the data sued to compute the Area Fit Index for each approach. <strong>Morphological zones of Ouagadougou as created by Grippa et al. 2017 a shp format.</strong>