… 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 …

… 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 …

… 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 …

… 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 …

… Google Earth provides high resolution imageries where various land use … land use of the
classification scheme of our work. The points that were not falling in the designated land use …

… The rapid transformation of land cover/land use (LCLU) is a strong indication of global …
The present study strongly advocates the use of combined S-1-2 data together with the …

… land use in Hangzhou (2018). It aimed to compare the classification accuracy of different
machine-learning methods in urban land use. In … for urban land-use planning and management. …

… The contributions of the work are i) land use and land cover classification using machine
learning models; ii) generating the feature set from the raster using the shapefile of the training …

… In the current study, different machine learning methods were applied to determine the …
The accuracy of barren land was lower than that of other land use classes, as observed in …