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Clasificación de uso y cobertura del suelo a través de algoritmos de aprendizaje automático: revisión bibliográfica

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Clasificación de uso y cobertura del suelo a través de algoritmos de aprendizaje automático: revisión bibliográfica

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dc.contributor.author Tobar-Díaz, René es_ES
dc.contributor.author Gao, Yan es_ES
dc.contributor.author Mas, Jean François es_ES
dc.contributor.author Cambrón-Sandoval, Víctor Hugo es_ES
dc.date.accessioned 2023-11-06T12:57:13Z
dc.date.available 2023-11-06T12:57:13Z
dc.date.issued 2023-07-28
dc.identifier.issn 1133-0953
dc.identifier.uri http://hdl.handle.net/10251/199305
dc.description.abstract [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_ES
dc.description.abstract [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. es_ES
dc.language Español es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof Revista de Teledetección es_ES
dc.rights Reconocimiento - No comercial - Compartir igual (by-nc-sa) es_ES
dc.subject Land cover es_ES
dc.subject Land use es_ES
dc.subject Random forest es_ES
dc.subject Support vector machine es_ES
dc.subject Artificial neural network es_ES
dc.subject Decision trees es_ES
dc.subject Machine learning es_ES
dc.subject Aprendizaje automático es_ES
dc.subject Uso del suelo es_ES
dc.subject Cobertura del suelo es_ES
dc.subject Bosque aleatorio es_ES
dc.subject Máquina de soporte de vectores es_ES
dc.subject Redes neuronales artificiales es_ES
dc.subject Árboles de decisión es_ES
dc.title Clasificación de uso y cobertura del suelo a través de algoritmos de aprendizaje automático: revisión bibliográfica es_ES
dc.title.alternative Classification of land use and land cover through machine learning algorithms: a literature review es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/raet.2023.19014
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation 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.2023.19014 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/raet.2023.19014 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 19 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.issue 62 es_ES
dc.identifier.eissn 1988-8740
dc.relation.pasarela OJS\19014 es_ES
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