Abstract | ||
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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. |
Year | DOI | Venue |
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2015 | 10.1080/13658816.2015.1008004 | International Journal of Geographical Information Science |
Keywords | Field | DocType |
land-use modelling, multi-label, machine learning, geographic information systems | Data mining,Geographic information system,Nearest neighbour,Aerial photography,Computer science,Artificial intelligence,Machine learning,Land use | Journal |
Volume | Issue | ISSN |
29 | 6 | 1365-8816 |
Citations | PageRank | References |
5 | 0.42 | 18 |
Authors | ||
4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Hichem Omrani | 1 | 89 | 7.91 |
Fahed Abdallah | 2 | 243 | 19.67 |
Omar Charif | 3 | 10 | 1.61 |
Nicholas T. Longford | 4 | 5 | 0.42 |