Segmentation and sequential classification of a synthetized image composed of spatial environmental data for the compilation of a soil type map

A unified, national soil type map with spatially consistent predictive capabilities was compiled applying traditional and newly tested Digital Soil Mapping classification methods: segmentation of a synthesized image consisting of predictor variables and multi-phase, sequential classification by Classification and Regression Trees, Random Forests and Artificial Neural Networks. Object based classification using spatial-thematic segments was applied to define mapping objects. Classifications were carried out on two levels to achieve better results. Performance of classifiers was continuously assessed and applied for the identification of best performing predictions, which were combined for the production of the final map.