A multi-label cellular automata model for land change simulation.

The use of cellular automata (CA) has for some time been considered among the most appropriate approaches for modeling land-use changes. Each cell in a traditional CA model has a state that evolves according to transition rules, taking into consideration its own and its neighbors ' states and characteristics. Here, we present a multi-label CA model in which a cell may simultaneously have more than one state. The model uses a multi-label learning method ? a multi-label support vector machine, Rank-SVM ? to define the transition rules. The model was used with a multi-label land-use dataset for Luxembourg, built from vector-based land-use data using a method presented here. The proposed multi-label CA model showed promising performance in terms of its ability to capture and model the details and complexities of changes in land-use patterns. Applied to historical land use data, the proposed model estimated the land use change with an accuracy of 87.2% exact matching and 98.84% when including cells with a misclassification of a single label, which is comparably better than a classical multi-class model that achieved 83.6%. The multi-label cellular automata outperformed a model combining CA and artificial neural networks. All model goodness of fit comparisons were quantified using various performance metrics for predictive models.