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.