Title
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.
Year
DOI
Venue
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 Omrani1897.91
Fahed Abdallah224319.67
Omar Charif3101.61
Nicholas T. Longford450.42