SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas

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