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:

• A Land Use - Land Cover map of Ouagadougou derived through SPUSPO. The classifier used was Extreme Gradient Boosting (XGBoost). 

  Labels :

  2 : Artificial Ground Surface

  0 : Building

  5 : Low Vegetation

  4 : Tree

  1 : Swimming Pool

  3 : Bare Ground

  7 : Shadow

  6 : Inland Water

• The training and test data used in  the study (SPUSPO and benchmark approach). 

The data are given in a csv format.

• The Jupyter notebook code which involves Python and GRASS GIS to automatize and efficiently perform SPUSPO in a large dataset.

Python code calling GRASS GIS functions for automatizing the procedure.

• The segmentation layers coming from SPUSPO and the benchmark approaches (in raster formats due to data limitations).

Segmentation rasters for each approach.

• The R code for optimization of XGBoost as well as feature selection with VSURF and classification of the whole dataset.

• Segmentation evaluation metrics.

A csv file with the data sued to compute the Area Fit Index for each approach.

• Morphological zones of Ouagadougou as created by Grippa et al. 2017 a shp format.