Multi-label class assignment in land-use modelling

Abstract

During the last two decades, a variety of models have been applied to understand and predict changes in land use. These models assign a single-attribute label to each spatial unit at any particular time of the simulation. This is not realistic because mixed use of land is quite common. A more detailed classification allowing the modelling of mixed land use would be desirable for better understanding and interpreting the evolution of the use of land. A possible solution is the multi-label (ML) concept where each spatial unit can belong to multiple classes simultaneously. For example, a cluster of summer houses at a lake in a forested area should be classified as water, forest and residential (built-up). The ML concept was introduced recently, and it belongs to the machine learning field. In this article, the ML concept is introduced and applied in land-use modelling. As a novelty, we present a land-use change model that allows ML class assignment using the k nearest neighbour (kNN) method that derives a functional relationship between land use and a set of explanatory variables. A case study with a rich data-set from Luxembourg using biophysical data from aerial photography is described. The model achieves promising results based on the well-known ML evaluation criteria. The application described in this article highlights the value of the multi-label k nearest neighbour method (MLkNN) for land-use modelling.

Keywords:

• land-use modelling
• multi-label
• machine learning
• geographic information systems

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Acknowledgements

This research is part of the SMART-BOUNDARY project supported by the National Research Fund of Luxembourg (contract INTER/CNRS/12/02, application ID: 6543047) and by core funding for Luxembourg Institute of Socio-Economic Research-LISER from the Ministry of Higher Education and Research of Luxembourg. Dr Omrani is grateful for many helpful comments and suggestions made by Prof Danielle Marceau from the University of Calgary-Canada. The authors are thankful for the reviewers of their valuable comments concerning various versions of the manuscript.