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.

Integrating the multi-label land-use concept and cellular automata with the artificial neural network-based Land Transformation Model: an integrated ML-CA-LTM modeling framework

“…where m is the number of cells used in the testing run. F1 is the harmonic average of Prec and Rec (Omrani et al 2015a). Like Acc, it is a symmetric function of the actual and estimated labels.…”

“…Machine learning techniques (Witten and Frank 2005), due to their ability to fit nonlinear functions, have been used to learn about land-use change (LUC) patterns (e.g., Pijanowski et al 2002aPijanowski et al , 2006. Machine learning techniques include various approaches such as artificial neural networks (ANNs) (Li and Yeh 2002;Basse et al 2014), support vector machines (Yang, Li, and Shi 2008;Huang, Xie, and Tay 2010), genetic algorithms (Shan, Alkheder, and Wang 2008), classification and regression trees (Tayyebi and Pijanowski 2014), multivariate adaptive regression splines (Tayyebi et al 2014b), among others (Bagan and Yamagata 2015;Omrani et al 2015aOmrani et al , 2015b.…”

“…Thus, it is required to develop a LUC model that can simulate mixed land-use classes. Omrani et al (2015a) have recently showed the merit of the ML concept in LUC modeling. These researchers utilized the ML k-nearest neighbors (MLkNN) technique, but did so outside the CA framework.…”

Assessing the performance of 38 machine learning models: the case of land consumption rates in Bavaria, Germany

“…svmRadial) perform better than decision trees and artificial neural networks, though our study indicates that ensemble and boosting models can achieve better results. In another multi-label classification study (Omrani et al 2015), knn was also found to be promising.…”

A multi‐label cellular automata model for land change simulation

“…Researchers in various disciplines, such as geography, economics, econometrics, and social sciences, have tried to find answers to these problems by developing a wide range of models. With the development of geographic information systems (GISs), computer scientists have become more and more interested in this subject, and have proposed many methods, including artificial intelligence (Liu, Feng, & Pontius, ), machine learning (Omrani, Abdallah, Charif, & Longford, ; Pijanowski, Tayyebi, Delavar, & Yazdanpanah, ; Pijanowski et al, ; Tayyebi, Pijanowski, Linderman, & Gratton, ), and data mining (Basse, Omrani, Charif, Gerber, & Bódis, ; Samardžić‐Petrović, Dragićević, Kovačević, & Bajat, ; Tayyebi & Pijanowski, ) to model land‐use systems.…”