Improving Land Use/Cover Classification with a Multiple Classifier System Using AdaBoost Integration Technique

Guangzhou has experienced a rapid urbanization since 1978 when China initiated the economic reform, resulting in significant land use/cover changes (LUC). To produce a time series of accurate LUC dataset that can be used to study urbanization and its impacts, Landsat imagery was used to map LUC changes in Guangzhou from 1987 to 2015 at a three-year interval using a multiple classifier system (MCS). The system was based on a weighted vector to combine base classifiers of different classification algorithms, and was improved using the AdaBoost technique. The new classification method used support vector machines (SVM), C4.5 decision tree, and neural networks (ANN) as the training algorithms of the base classifiers, and produced higher overall classification accuracy (88.12%) and Kappa coefficient (0.87) than each base classifier did. The results of the experiment showed that, based on the accuracy improvement of each class, the overall accuracy was improved effectively, which combined advantages from each base classifier. The new method is of high robustness and low risk of overfitting, and is reliable and accurate, and could be used for analyzing urbanization processes and its impacts

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

[EN] 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.[ES] Los métodos para la clasificación de uso y cobertura del suelo (UCS) han mostrado avances importantes en los últimos años, como la incorporación de las técnicas de aprendizaje automático (machine learning-ML) que han ganado popularidad y aceptación por sus resultados. Sin embargo, la falta de consensos metodológicos ha provocado una aplicación desordenada de los métodos ML en la clasificación de UCS. Por lo que a través de la revisión bibliográfica practicada se identificaron puntos de la forma en que se están implementando los métodos, así como posibles implicaciones en la clasificación de UCS al darse de esta manera. Para dicha revisión se utilizaron únicamente artículos científicos publicados entre el año 2000 al 2020 y que consideraran alguno de los siguientes algoritmos para la clasificación de UCS: k vecinos más cercanos (K-nearest neighbor-KNN), bosque aleatorio (random forest-RF), máquina de soporte de vectores (support vector machine-SVM), redes neuronales artificiales (artificial neural network-ANN) y árboles de decisión (decision trees-DT). A través de los resultados obtenidos en la revisión bibliográfica, se reafirma el potencial de los algoritmos y se identifican puntos de mejora para la aplicación de ML en la clasificación de UCS, especialmente en la integración de los conjuntos de datos, la parametrización de los algoritmos y la evaluación de los resultados, generando a su vez una selección de buenas prácticas a partir de las recomendaciones de diversos autores las cuales consideramos serán de utilidad para usuarios interesados en estos métodos.Tobar-Díaz, R.; Gao, Y.; Mas, JF.; Cambrón-Sandoval, VH. (2023). Clasificación de uso y cobertura del suelo a través de algoritmos de aprendizaje automático: revisión bibliográfica. Revista de Teledetección. (62):1-19. https://doi.org/10.4995/raet.2023.1901411962Abdel-Rahman, E.M., Mutanga, O., Adam, E., & Ismail, R. 2014. 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