Multi-label class assignment in land-use modelling.

Hichem Omrani, Fahed-Olivier Abdallah, Omar Charif, Nicholas Tibor Longford

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)1023-1041
JournalInternational Journal of Geographical Information Science
Issue number6
Publication statusPublished - 1 Jan 2015


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

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