TY - JOUR
T1 - Multi-label class assignment in land-use modelling.
AU - Omrani, Hichem
AU - Abdallah, Fahed-Olivier
AU - Charif, Omar
AU - Longford, Nicholas Tibor
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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.
AB - 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.
KW - geographic information systems
KW - land-use modelling
KW - machine learning
KW - multi-label
U2 - 10.1080/13658816.2015.1008004
DO - 10.1080/13658816.2015.1008004
M3 - Article
SN - 1365-8816
VL - 29
SP - 1023
EP - 1041
JO - International Journal of Geographical Information Science
JF - International Journal of Geographical Information Science
IS - 6
ER -