Selection of classification techniques for land use - land cover change investigation

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Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers (2014, methodology): “However, the MLC technique relies heavily on the quality of training data (Srivastava et al. 2012; Richards, Jia, and van de Meer 2006), while the unsupervised classification techniques such as K-Means and ISODATA are often illogical, which makes it difficult to label the clusters and transfer them…”
Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations - A Review (2020, methodology): “The original SVM method has been launched with a set of data, and its objective is to find the hyper-plane that can separate the datasets into a number of classes, as the aim of SVM is to find the optimal separating hyper-plane from the available hyper-planes [77].”
Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application (2013, methodology): “…modelling problems like river level forecasting, rainfall runoff modelling, daily evaporation, sedimentation yield, rainfall forecasting, ground water modelling, reservoir inflow, water quality prediction and water resources management (Han et al. 2007; Srivastava et al. 2012b; Islam et al. 2012).”