… Upcoming challenges of machine learning in modelling … of machine learning in accelerating
and advancing studies of LULCC modelling. Despite this, we believe that machine learning …

… consistent accuracy level than the other machine-learning algorithms. Finally, this review
concludes that the RF algorithm is the best machine-learning LULC classifier, among the six …

… land use change. New technologies, such as artificial intelligence (AI) and machine
learning (ML) have made it possible to model and predict the nonlinear aspects of urban land …

… Remote sensing software is used to quantitatively detect any changes in land use. In
addition, the land-use map produced from the remote sensing software can be extracted and …

… class influences future land use apart … land use class for the time interval 2003–2007 is
land use class at the year 2001 and land use class at year 2003 represents previous land use …

… and emergent machine learning algorithms to classify land-cover and land-use using
multi-temporal Sentinel-2 data over a complex boreal landscape. Two of the machine learning …

… were able to capture land use and land cover with greater … to intercompare different machine
learning methods for LULC … applying machine learning-based hybrid models in land use …

… is the major cause of the land use changes (LUCs) (Marshall … farmlands and several other
land use–land covers (Dai et al. … been applied to model land use–land cover change (LUCC). …

… comparisons using different machine learning models. Machine learning has the potential to
… using different remote sensing data with machine learning techniques for different types of …

… Recently, machine learning approaches have been … , combining different machine learning
methods with complementary … the complementary advantages of machine learning methods, …