Classification of land use and land cover through machine learning algorithms: a literature review




land cover, land use, random forest, support vector machine, artificial neural network, decision trees, machine learning


Methodologies for land use and land cover (LULC) classification have demonstrated significant advances in recent years, such as the incorporation of machine learning (ML) classification techniques, which have gained popularity and acceptance of their capabilities. However, the lack of methodological consensus has led to a disorderly application of ML methods in the classification of LULC. Through the literature review, we identified some points in how the methods are being implemented as possible implications for the classification of LULC. For this review, only scientific articles published between 2000 and 2020 were analyzed that incorporated any of the following algorithms for LULC classification: K-nearest neighbor (KNN), random forest (RF), support vector machine (SVM), artificial neural network (ANN) and decision trees (DT). Using the results of the literature review, we were able to confirm the potential of the algorithms. We also identified areas for improvement in the application of machine learning to the classification of LULC. These areas include the integration of data sets, parameterization of algorithms, and evaluation of results. Consequently, we generated a selection of guidelines based on the recommendations of various authors that we consider will be useful for users interested in these methods.


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Author Biographies

René Tobar-Díaz, Universidad Nacional Autónoma de México

Centro de Investigaciones en Geografía Ambiental

Yan Gao, Universidad Nacional Autónoma de México

Centro de Investigaciones en Geografía Ambiental

Jean François Mas, Universidad Nacional Autónoma de México

Centro de Investigaciones en Geografía Ambiental

Víctor Hugo Cambrón-Sandoval, Autonomous University of Queretaro

Facultad de Ciencias Naturales


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